{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-05-21T01:26:56.626382Z","iopub.execute_input":"2023-05-21T01:26:56.627006Z","iopub.status.idle":"2023-05-21T01:27:00.537833Z","shell.execute_reply.started":"2023-05-21T01:26:56.626949Z","shell.execute_reply":"2023-05-21T01:27:00.535936Z"},"trusted":true},"execution_count":18,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[18], line 12\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Input data files are available in the read-only \"../input/\" directory\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dirname, _, filenames \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39mwalk(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/kaggle/input\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m filenames:\n\u001b[1;32m     14\u001b[0m         \u001b[38;5;28mprint\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dirname, filename))\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:377\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 377\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[43mentry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_dir\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m    379\u001b[0m     \u001b[38;5;66;03m# If is_dir() raises an OSError, consider that the entry is not\u001b[39;00m\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;66;03m# a directory, same behaviour than os.path.isdir().\u001b[39;00m\n\u001b[1;32m    381\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"code","source":"import torch\nimport torchvision\nimport torchvision.transforms as transforms\n\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\nbatch_size = 32\ntrain_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\ntest_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T11:05:16.733974Z","iopub.execute_input":"2023-05-21T11:05:16.734553Z","iopub.status.idle":"2023-05-21T11:05:32.901618Z","shell.execute_reply.started":"2023-05-21T11:05:16.734515Z","shell.execute_reply":"2023-05-21T11:05:32.900609Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\n\n# functions to show an image\n\n\ndef imshow(img):\n    img = img / 2 + 0.5     # unnormalize\n    npimg = img.numpy()\n    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n    plt.show()\n\n\n# get some random training images\ndataiter = iter(train_loader)\n# images, labels = dataiter.next()\nimages, labels = next(dataiter)\n\n# show images\nimshow(torchvision.utils.make_grid(images))\n# print labels\nprint(labels.shape)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T11:05:32.903906Z","iopub.execute_input":"2023-05-21T11:05:32.904914Z","iopub.status.idle":"2023-05-21T11:05:33.537129Z","shell.execute_reply.started":"2023-05-21T11:05:32.904878Z","shell.execute_reply":"2023-05-21T11:05:33.535638Z"},"trusted":true},"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAigAAAEqCAYAAAA/LasTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9Sax2a3bXCf6ebu/9dqf9mttHZ2OHaewscDorhRhkWZCqYoCYMESMMQPMBBhkKkcoRzCAnKUyRykQKkolFZKlSkuFCuRUFkHSGDcQcX0jbvc1p3273TxdDdaz9/ue716ZCCcQ4cizQie+e855z/vuvZ9urf/6r/9SOefMoz3aoz3aoz3aoz3aj5DpH/YFPNqjPdqjPdqjPdqjvWmPDsqjPdqjPdqjPdqj/cjZo4PyaI/2aI/2aI/2aD9y9uigPNqjPdqjPdqjPdqPnD06KI/2aI/2aI/2aI/2I2ePDsqjPdqjPdqjPdqj/cjZo4PyaI/2aI/2aI/2aD9y9uigPNqjPdqjPdqjPdqPnD06KI/2aI/2aI/2aI/2I2ePDsqjPdqjPdqjPdqj/cjZD9VB+Tt/5+/w1a9+laZp+IVf+AX+1//1f/1hXs6jPdqjPdqjPdqj/YjYD81B+Xt/7+/xy7/8y/zX//V/zT/7Z/+Mn/3Zn+VP/ak/xatXr35Yl/Roj/Zoj/Zoj/ZoPyKmfljNAn/hF36Bn//5n+dv/+2/DUBKiffff5+/9Jf+En/1r/7V3/VvU0p89tlnrFYrlFL/MS730R7t0R7t0R7t0f53Ws6ZzWbDO++8g9a/O0Zi/yNd0wMbhoFvfetb/LW/9temn2mt+cVf/EV+7dd+7Quv7/uevu+n7z/99FN+5md+5j/KtT7aoz3aoz3aoz3av1/7+OOPee+9937X1/xQHJSrqytijDx//vzBz58/f85v/dZvfeH1f+Nv/A3+m//mv/nCz//yX/7L1HX9H+w6H+3RHu3RHu3RHu3fn/V9z9/8m3+T1Wr173ztD8VB+UHtr/21v8Yv//IvT9+v12vef/996rp+dFAe7dEe7dEe7dF+n9n3Q8/4oTgoT548wRjDy5cvH/z85cuXvPXWW194/b/LEXn69CkffPDBv/fr/FGzly9f8sknnwCglObJ8/ep6ka+B7RSoMu/SskP37QsXyllcsqklOj7Hu8Dm+0a7z1duyemSIxB3kYpUoqklPDek3ICJC035hCPqUzWWrTWVFWNs47ZfE5d18znc1xVYazDaA1KkWKGnMlZLg3KtWX5Silw+/pTgh8AOD+d8fWvXCJ/kOU+5YGUNyg/f8Csyodv89G1vvkyuREyR49Oyf8dP8rvh7SVv/Afx+93uOTphwpyTqA02tZc32753sdX02urvMOoQFNXKKXIJECRlcEZhzWOqrJYa5jNa5RS+BBIORFiZBgGum4ghEhOGeMsKWVe3dyQYkIpRe0qZs2Mzf2W/b5FaYXSiqapcc6xWi3oh4HddkvwkRgS77z7FsvlAmM0Xdfz4YffI8ZEyvDs6TnPn54DiZwir25vyWTeefsJ1hh0tuz3PZtNB8qQMbQ+E8sz66PmdVvJ8z8ah3F8tFIopWQuTc/04aSX7+V1SoEev7Rw2XLOhDIPVJa/1ePATW+Vj95vnGaKXF4wvvc0T8o1jH+Vc0aRgMx+t8YPPcH3kBKKxLxxPDtfTp+xOrukni/RZRkbpeXa9eF9U8yklNFaoZXCGM3xrSvUNOvzeF9a/jbGREyZEBNaKbRWWKPRWk1/F2MmpoyPkZggTGtSrkNrcOVvjNbkDKmsrTw+dyVjlJE1nfJhbDOwv36Bb3cAOAMXK4XRCq309EytdShjUFqTc8Z7T4yREALGWJxzZOS6UozyGeU1wzCgtMZYg6sqnLU4Z1FaE4InpUzwAa01zrrymZqUIjlnYoyknGQfRPa7cT/MKcnnlmetlVxfLs9pnKQqQz8MhBDY7/ckDLk6Y5xcigGUJydLzpoYZPvSSsm1G1PeL6O0LuNzmItaKbRR0zMgj+9b9v/xOtCgxrGRn1ljUEoRhkAq++34t+N9jrP4+DjJZZy11nJNZeLJejq8JqU8zcmQwnRu/CD2Q3FQqqrij/7RP8qv/uqv8mf+zJ8B5OZ+9Vd/lV/6pV/6gd/vgw8+4E//6T/97/kqf/Tsn/yTfzI5KNpovvITf4TT86dAQiuwWmOMRluFNvowE48tQw6Z4BMhyEK/ublhu92x/+h3aIc1t+sNg+/o+3bajLwf8NGz2a6JMZZrMNR1XRZmmXwKmmaGc46z0wsWi4rT+RmnFxc8f/4Wq9NT5sslztVopRmGUBwlikMCMUbZHFJi6Dt269vJQXn72Qn/t//LN1EkcoygNUrp6T5zOXTKGx7dtyzO400ky5lx9JKy5RwtVJQ4g4eDL48+3humHnzc+H7lo6dnU84xeX7lBBrfOxJQ2mHn5/yLX//ekYOSafIdDXuezE+xRhMJZGVI2rFsVizqipPTObNZxfO3LjHWsG13+OBph57b2zVXV3u6dsD7yHyxoA+B7338XYbBY5SmPjnjZPWc67tPuPrsFcppjDM8eXpOvVry5KTm7m7N7fZjdpuOfu/5uW8+5YMPVtRVxdXVLd/6x/+GthsIGT64/CY/897bgCfEzP3tC7JK/KE/8BbzeobNDS8+u+Oj37kDMyMpy8sEMcgD23nDb14vyUoOrnEcxsdZaYPWmtra6UBTHB/UatpAjbFljWScBQeEHIkp0kaZdwqNBoyMJkqNI12+puWk5KApA6mVLodXmSflFEjTHEioHFA58OJ6y+a+Y7+9IQWPwfP25YqnZ4tpHly+9T6Xb7+PNWCUorJyn85MrhP9EAghUVmDNZpZUx18dcohmsUpEJdLYS2knGmHwOAj+y5ijcZZw7yxVM6glczXto8MIbJuBzqfaIdITJmUoXIaaxTLmcVaTeMsKYOPTK/RRqO0wmpZF0NM+JQZQiYpOVA/++f/eHJQKgffeEtRW4OzFqM1RhvqxQJX1yjrSDmzWa8ZhoHdbkcza1gsltN9Dr04At0u0vee29sNtnJUsxknpzMWizmr5QJrDfv9Hu89+22HcxXz+RxrLUYbfBAHp+1aYvR0XUBrhbO6XJcixkCMUeaigspYcWp8IEVxILUSh/X2dk/btnx+/TmehuhOJydWqRbFlpgW5GjpW9mXrDFYa7G1gQQ5gbYyx8Z9xRhxTKtKk7I8e7ISR1sdOe0otDYSBJFQRgKPedOgtWK32YvTGtM0za01xSFOKJUxR97vuN9rY7GumtaYH8TpA9l+Q0igxElpfccQhy/smv8u+6GleH75l3+ZP//n/zx/7I/9Mf7T//Q/5W/9rb/FbrfjL/yFv/B7fs//Y1X0lA1Za4ySKMpZg9JqioTk0MxT5BFCYL/b03c9281OnA7vubu7oW33XN+8outafNyBSrhKEWPEh4gPY/SdJNJHQZbIWBwdX65HYYwi58Bmc0fft4QwsN1tGIaBp8PbnIXMaqVw1pHCGOfJPVinCVqi3JQUOZkHkWHOkRR7VIoQE+hDJCu/HxGUowi4nGgpJnGGYiSXRV+e5BHGMkYZmjxGS1PkngvqUhyP8cLyw+hlilyOr2ccNY04JUBU4z5VIpAcUTaS7UCO4cFo29pRuYbFak5dV8znNdo4jGtw1lIZCyqR6fnuxx/iQ+B+uxEkq5mx3mxZb9bU1YzZvOb165fs9h06G+Z1xcnqlBwiL1++JqbE8mTJbDnHVhbnDCEmPvv8Bbvtls1mR04aU9d8+8OPeH19zTe+8XWCj7z7wQd0fU/ve95+5y3eeudtPv/8E9abPd5nhhj4zd/+iPOTM7769lfJylA3NT5rYvqSCEurEv2VTVfJCaqOn//onOY3PEDGSF6hVEYrMDozq2uW85p+6BhCILY9UYnLoQFXdmlFOWgL4qIUoAUpGGISZCBTon5BwHJWBQGU61Uc0EBGlIFMLpFwTHna1Kc5zGEKZ3V8f4db08VpM1qhtZ4QEHKe7nk8uHM5RXV5L2c0OUFlM9YYnNU4Y7AFIUg544wio6mdJeVIiBmj5dqrSpyi2pnJwQkxE4+QUFP2IWd0ce8ETog5Y7QsEH20sK2xnJ2fsmgc89kcShBRzWYY6+iD7DNWG7KxNFWNyrDf7SfkJsVIzgmj5ZqauhLHpWvZkOm7jso5tJ7h6galLV2bCBE22w5V9tFcxqgfPCEG2v2AMZqmVjSVpapqjJO7SiRQispU5JgZtGfoB7zvIKYSuGmMcdT1DJVr2qP9LMV85Nhk2Q+zwpdgaUJtpvl9QHi11miVC5qoUUYLolKc9ZwpwZgAAFrJfM45kWMiek9WShBDpVBGFYS5bI5JoXR5f13QSqMhF/TOGHGSigNNCtN8VmQwWSAkBSp8P7jzF+2H5qD8uT/353j9+jX/1X/1X/HixQt+7ud+jl/5lV/5AnH20X4XK9H3CLNaY47yBmWylQkz9J6+77m5vmW323J7c8Mw9Hg/sF7f0vctm+2aEAMpDSilcJUldYEYAyGUiKHsQErJDppTJMWAHwa0kYkcg4Ic2KdE3+3p2j1D35NRWNdgTU1dNSg0E/CiBPWxVmJYhTgoKR7QEZDFk+KATglCLHj8uIEfnIzpEWklzkjOpCAbRvLxCEE5wPSHP4I8nkgTXF+i0dHxmHyTPGWVxt9NCaFjZ2l6X/UA2BrfZ0o9JUh1EOfr6IJMZXFNZracsZjNuDw/wzqHq2dARJHY7Xd0Xc+nLz5ht9uz3m6o6obLJ8/Y7fa07Y75bEbTOO7ubllvthg1p67nnJ2cc3d7x/XNNUZrZosZp2enuMrR9S0xBq6urum6jrbrqSpJ133y2ed8/uolZxeX1HXD0+fPGXxP1+148vSSi8sLPv3sE7a7Fu8z3RD46KMXtE8jbz/5ChmNq2vCEMkpMkb70/xGFUdkTNM8DELyhKgc0nAHCPz4tbJRajJ1ZVktZmiTMYMSCB4wKmOA6vDx2IJGTktLQcwZFQKpAHVGZ7SGGOQgII8JuDEdlA/jC9OBnVFT6uPhTVGcXvXAKZFU0WG+jiiF1nJw6ALJj/edy/mQioMyprhkr8jFMdE4K4iI0YfnZYzGAlVxPuzkaEDlDM4IsmOMnlASHQUd0ZTUgxLHKWdIBhIJY1KJuPOD8dFGszpZsprPOFkuiSGSYsTWDdpY8nYvqUmtycZQOcfgA/3QkZI4VUYzpf6s0dSVox8Ghr4nhkDXGs7PL6jqBmMdOWtQPSEEhr4ryzRjjCByQ0GY2z5ISlIlnNUo5bBOUiuRODkoKWZSNviQiAmij+QQZf0aS103kBzdUZomJXFS5H7zAfgtc8JqL2NrtKR1j/I7SqXpGSqlURicG1N1su/EKGn8lBIKjVEyH1LOpBCm4GtMl4qjl1ElCFW5zDVFQZFMue48zb3KiYPih6OATR/P9VTQyB/cfqgk2V/6pV/6PaV0Hk1s9PjHKEppRdu2bDdb2raj7wfarmUYBl6/fs1+t+PVq5e0+z3bzRrrdMldJzKJfmjJOaGtwjmHtRWpskCmHzq8H4oXLg6RNhpnLSkEKHnbpCF6BcnijIUMvu9Y398JNyEp9rsWBaxWJ8xnS7l2MhqJcp1VWG2ISUF8iKBQcrOMUecUNY8H0rhgp/+UMy8fcvBZxRJdwGFDHw9B+eOsVFmAI8cmobPA1zxwhA4HhirR17G3ImmiB9j7IVUw5unLu6WQURjID9NFSsHi9JSzE8U7H7zHvK6ojKLbt7z8/BPWm3vW2zUZix8i//M/+sd03cBP/cxP8+47T/j613+SYejYt3uMNpDha1/7gLb1ZLVit+/53kefMnhPTBJJaQO39zf44Pnu9z6mco6vf/1rnJ8/4d1338cX/3C9ucf7gV//zX9LVddcXj7l9OyEb779Fera8e3f+R0++u6nfP7iJev7Fp8SKVfc3Qc+/uwanTXWOfb3d9xvt3j1BCh8s/E5jlyjo01Ufi1Qc1RxGruRR6D0hCEKipFknK1WGCIaT6Ui2iTmFaQo0b5RUOnD8BgrkL4Mo5z4iUxNOXgTU3onBkWK0LaeEBNdCIxQjEL4Js5qaufw1hJyJPhD7n+0mCLeB3LWJAmaJ7RkctoY/ZhMzokY0sH55djpGefoASMcI2KjdUFVRnepLBelsEaRs8IosBqcHrknisqKQ+SMHFwqZ1RJZU1zFoUhY7UShCPE8jmppNAe8rqUUlTOojX0fSe/V+UatRYOVduy3+3w3tMPg+xv7YB1FVobfI4olWkqizOGp08u2e17bu62rLc79t2WPn5KVV/JaOSMH+J4ik7cihLbEUJEa81iuWJImfZ+4OrujpxuOD1dMJvVk6PSVIIIGaUxrqKZzxloCXkgI47v6ekp+2BZ7w/3PQyJvovoHA9TvqBdRhdkZNqbNGiDQgsCW3gw6IwiohR4L/N83FMyJdDL4nSkBMYZnKmIPpJiYrfbQs64ypQxQrDEbEgodAasOqCY5IIkZlKIWFPQMG0KF0j4MpWzhDAw+PCFOf792u+LKp5H+11s3EmRxTX0Pff392w2W/a7Pbt2T9/3vPj8c3a7Ha9fvaTvWvb7HU1TUdWOymmUBh96UOCykFxzzlPaRmC9JJBiQW1Ggp0aN+8xBZJScQLEC8gp4Yee3W7H+v4eYyzbzROssTT1bPLexwgPFNnIMjFGTef7eLsT/j5uvvnh8xgJeg8tT84HD3595Mkc/34CokpUWg4FVeB5xeHcnN5JKckXl+cwhtCHyH560+mzjrktY7pndG2OzTiHaxyL5YqmsoSRX7LfcX9/z9XNDVDRD5HvfvdzBh/5+k/9DMpUzOdLmqZmNp/hB0nrnZycUFUBHxr6NrC+2xDH1IZVqJzo+5au67m9vaVpZqA0ddNwdnFB23m6weNjQPeWu/t7nOs4O7/AVY6LizOGds/d3R2b3Z59OxAT5KwJQdEPme22o3aOpqrwYaDr9qQ6TR7bm0P45vcjITMVR0ZlCpEwoQsKN86SEdOQDT+SU0ATMSQqA0lBNXI+Cngmzrg4tjKQ8pVLuigd+05AVEoc9EFeOpDk6FcKXca3shZfOSrnUDkSvwwVSrkghYBWxCipoxFFAj3N0UyJelOSWVnQjJTz0dw8IC95mpCHtXvsuUw02TH4Gb80WDOmbUp6aVy3jI7gw5hAl+d0dGfl50wE4OOx1UrIp32QlIrRQlhFaVKIxBAJPuC9Z+h7+m6gbTtcTBgjz1OpjNWglcO5CmMiYPAhi+OYNyjTEsoCVWi0Kp9VUnEpliScFo7NLFtiCLT7QPADg+9BaUIE4ySlHWpJlzVVJXwmZdCFI5Wz8JWaxpC8QbWHACQlRYyHPWl0sPWUrjkOvMbdrzjqU6pwHN1UuFTHQZGatiL5XtJG1lpJeUeIIYgTlaY4gGkRTty9IweufJ+SPKvoMqpwjwQsVOUaBQ2V1Ce/J3t0UH7fWiYlgSBJ4kD0KfLJxx/zr//1r/P69RXr+3WB9yKb7ZbgPX3fSVomBYKXqGe36Ykx0HYtKJgtZtR1TbscClKiCNGTCVPFhzUOcppQlVlTo41BK+Gt5JTZb/coZXC2AgKogevXL1nf35FT5PzsnJ/8yZ9muVxycX4hyIyhbMaykFLUPIi1jlaLzPl0+DmURfFgSXJ4pRwwSiko0d9YjTFmbyb3YARoSmQj780BJeFo0R7B6sqUX8ajy5guZ4Rox8PmgLyIU5bAlCDlDUh06DN9l8DUtMPAr/+Lf4k1huVyCaomxYaPPnrN1fWazcZRz0548vwncPWSb3/7Y54+Peetty559fol+7ZjNluQYsu3/un/j5ubNZ9/dgPGoqyjmgk5L4aBlCKXz57S1A1ZG5StaGYr2uGWwXd87Wtfo64b/tW//JeF09QyDHuGoWez27G+W6Ndw+nlU5Zn4GPi5n5Nioq+81LJkgJDt8V3W7SLB35fQQuOHfAHVpCuePRznVNxQkr6p8DdhoRRCqshDi37dYtGDrSZlTGorESetjgTqsDoKEll5pxREv5PjrRSqnCaEpVxYAy1rggh49qCdGiNUZIGmplLhmHB5n5G1+15/SIyb+o35qs4P6qk/EJOBbYXNFHro1cXJHB0Ykw+pGImGtZ4tuXjn6uJAyIHizxrW4i4ighGUzkNyqBK1Y41enJU1Hgh+TAWunBRaldeqxUxl/SAGv9WPuPYeUkp07eBLnk267Wgl0qxXK6o6go/BEjCy4ohsd3sWW+23NzekQs3aVY3B06Nq1iteu53LS9v1gQ/EHxkvb0hpkTMBmMMy/lCUnhOrjNm2K13pJT5Iz/3c1RVxYvPX7Pfbbm/vcIahbWa65S5v9+x3q6JKTGfzajrirPVkspoKquZ1YaqmTFX4nw5p1i3GXXvp/2qrhdYUwnnLINz84KUaBnYFKfBTiWnqEsZmnGVVO01jhQjMYRpjGN5v9EZdE5Ita5U+mmtiYWPUs8aeW9V0HFjUNqilMX3vTiHaRDSs40HhCkmYsy0fURrJfsrGZWFNxNiJoaA91/kWX2/9uig/H61DN4Huq6n7/aSv4+Rq+trrq+vuL25Zr3eTKWJfuglGhlZ2QWhyDHSdV0pQ+1lYzCWnIX57Sopzcul5GWi940OQpKfa60nODYWEmpMUsaYdRaGuPdSmeMHbm+uSTHw/NlzlMpcnJ8JNF8cjDwuLv1FNGQ6uw//cfhHHZUUHz+u8tp8/HKtJEVUotBjv2ZCS45QkkPENx6Y6kFuVU0ENgU6M+Hz058UNEaNMcbhk8ZrHCH6B9eeYbvZoSJsN3tU9oUH4pghFR37Xc9u37NvA9rUuHrObL4kJvjk489J0VPXhuADxhh8J7yiFAdyHEgxyMYdEzhQTuOqqjifM6qqoa4brHVyoKVEzpHFfMZyuWKxmOMHy3Ixo6osfvC0bc92uyfEVNIuQIy0bYsp95ljJCBzV71ZVsUhjfFmaucYdcjTSTwmK8aZWgiqI1GPXEiFSeatkldbbdAabIn4C4+58CQK/qKEHKDL+5vpxC8RYqJUSigqYzAKQpTqihGN0DqjmorKgkoLKqvplgtmjXtjso7EWjViMMJNyoJAHBy1gp4gED7pMB/z9FZjMaysUXF+CveloC7jnMsjEsV4vQi/xAi6cFzFMiGryBIygM2ydlUh2ZpCrFQ5Y8ohnUeHvDhJx2PY9wMkj/d+Qmn3+x390Jf95FAtqI0cstpoBh8JKQsaoo2kvE2gD7Bte7a7limFPDp6GLTSxOCJMdP7RCifEUJAK1O4dbC+v8f3nSA0SFosRk9OibbtCDESY2YYAlaJg1RbjR/EUVk2FZW1khKyCfBHe4Zccx4dhJIuzEfVOmpMN0+PqxBVsilz5bDFFB7t0ViOsVFCGXcgJo/pUwVKGzRj2fSIyKgHe2HO+fC+ZWuDEcUrKcWyr40pUYqTrzmM+Q9qjw7K71PLOXN/d8962/Pr//JfEPyA1XBzfcXnn3/Cfrdj6DvmiznWOprGApYUx4mfCD4SvOfm+pbtdgeYomFSM/SJvh2YLxrmy4ZMQpuxQmGcqGWTG2FJJYs+BCF9keWgTjoTwkBI7cSB3G3XNM0MReatt97mredP0LpBWzNBrVIW+jBXXe4eQU4KJslhQeQRjh8XH2UtpsOfqqIrgRInQ/aGQlAZj7dS4jMt/iMvZUzFGPUQRYHDJiLQfh5XsPwpEhVPJEYOBy4ZUk6oHNFHHJbx97/9r36bNKw5rysWixptK7K27Hzk81dX/Jt/8x263gGW88tnnFyccXZ5yqvPPuN//n/9v3n77Uu++rW3+Ln/5A/z7nvv8K8/+bfc393z7tunzGrN1dWaTetZb1vszDGvKt5/7znL+RxrHMZYmlpKOnfbHX7oUGngbDnn8vKCD959B0h87WvvEoPn/m7Ny5dXfP7iJU09wxpD1/fs93s+/PDf8OzyCT/11Q/IwdPtW1SMNMbgJwxrfPiH+famY4Ie0YI3HNXyt1O5ucookzBaEBSrEoaIVZKyNFoOWzNuqNNJkEv1TiaXWk8pRS5ObZLKK3wkh0Q2Ca2lygOrqa0jpTyV5SuAqgYq/LLCe8/p3OJUQKlDCWYqgTNlbaEzWQsJNRdnQnQyIMZMVomgNVol8pQfU5Aj5EzX7kkxYOtGUA9tiTHRD74gDvK90YqUjgiRClRlcFlTZ4Mp5aRTZVrJBygla8hZ0U7JOQvZXSlCWaKNHUnnTCWxx0MZQuDm6hpnS6q38L+urq7oug5bVcUhcWiruXh6RjWrcLOa65s1223Ltguk6Om9VB623QshWCvFcuaY1Y6nlxc0TU1WlqEfePXic/Zty916QzcEhhD5ylfe5eRkxmeffkTfD3z4b77LctHwja8+x+iM0olhGAgho3OAlNju9nS9J0YhYqscafdrwtDxtffe4ex0xTcW76C05QA7jfMalCrk0+xlvgc9IXVKCwLinDiO+/1AjIDPEA19jJIS0hqFVB/mcg1aZ8iRoR/QzNB1RfKRUHStyOIUJRQppTJ+EaMpxHAJZq3WE98xF2ffWVP0rMZgTuarVBSKM+oHhSfjh4RPBzT6+7VHB+X3qWWg7z2hzXz26af0XYsh0vctMQxALFBwQqlEXZnixeqS3454kKg1SyWOKpFRXdlS5iv8kZwyVlswhiGmQlCV3UUVPH6sFpKSGRjhb4CUokTnKcr2rhUxePoOrq9eY7Ti1cuXrFYnnJ1fTjl9azX5DQEqUYiqIOmHWPfkSRQBMw5RNUmgfiGT5QeOiER85XVfgl5IhauaSJcHh+MIXZmCjfG5lDA8H5cvjw5KeW4lTH9QGZuYNvzjs1YBrqpQeoZxDpRis25F+Ohuw8tXV7y8uWG+eEY9b3jn6ZLV6QnkxDD0rLc7zvoVMcHgE23ruV/v2G47Tk4uiLnh/EmL3vYE0+GqOaiKlGtirgheoSPEHCZHagiGkGpeXt2z7zL9EKgqQ900dG2m7Xq8j+Ss6QfPoERLwlrLarGgqSvC0EP0pGEgxcShpOkwx8WZVg9QlC9wNo7+7/h/KQtCoEfUqnApBPYWJEXljGzPBVJXSuDt8uAntKsM++ic6hJzqiSoCrqkLxSSHskJqzRJCWozojmj+I5RgFEsZw0qD5CHB/eT8qG4fURHUhI0KGepqhGvReZaTOLEMB78I8qVAtvtmqHvmS1XaGNQxuJ9ZN92zGczzGxOzvaA3FAcclUE4HJCpUyMQnY02hQRv+KqFbQFRh4H0/xNZXFZPR6cJdX6ZVZQGVPSTCknfAz4GLCqQltD3VSCMGhNFWrqIeCqHmMjvmsZfGDXeUJItN2AdYamrgsnxJJyJoTI4AU19t4z+MAwyOHprOFkNef0dMH17YbdriUGj8oVtdWE2DN0HdrUVLXj1CwIMbHeDTJHciBRHIuSMt33Hr3d8+L1DX205GymWzY2YXVAp7IPWYWobeoS9GmsVVinqCoraZpCPFbKlt+7sncX3odSUhWXJW1JTlJ55WoqW/R7ssIVdG1WSdAaU0FcCk9FK433cgZU1h2EOZUgKGNJ80ieFnQYrLZkpNotVZrYWIb7SBc8P6g9Oii/Xy3Dbtuza3f89m/8FtvtPTm0rFYLLi5PMaZMMp3QKjKfOxEiKjnz4D29VTiT0URyGkRx0SnOVjNijOz2bQl3MnVTY60m+UhEoM5xExuF1XKOE5Snx8UC+DCUCp6EVg6jpfJnCIGPv/s7bO7vOFme8NZb7+Lsgrp21LUTqHx0rEbTFuolKkdIvjgkCUKAnMg5TFDlAbrQxQkpO6SWRUuKBTJ+w7E4fCc6HKXkeMJySpSo9NFmzuHjppMsy4Z7UHoBsi4VD4hg0kSAk9cqo8Acnt34h2fnF1RaHI8UPS8+u2a/37Pb7/j2dz/mOx9/ws/8zFMunpzxB/7AT7JcLUi+p93vuNtueSsmXLNg30Wub3e8eHFL2w28//WfYnYaWQ8zbu522Kt7KbFG04WG3Fd0XQ85YkySKgVjSMmRs+XXf/sTVE6cnzWcn69oZjP6fmCz2eF9wmjHbr/Hh8Dl5RmzxYL33nmbxjm63YYcAymIwFbKh2dxPM9Hjs+bHJTj1M6blgokrZQgeGNVhDUKC2jS0ZfwVFKOqGyPiN/jHEqoDLpcn1YKg4Usc9pqi7ZqQhhSlo1YYzAy5OKkZ+GDZTIWuR63WpCCZthtD/d1pCUx3nfWCRVGhymTk8UayFnSSEpHdKn+Gg+NEALB97x69YLddsP55TNBIoyl73vu7tc8efKMuqolGMFIugpFynoqXxb+QGboOobBM5stsMqW+5UhGCv7xgERBdpELENkjCBjoYxLeiOdp5RCWYN2GmMtIXi8HxhiYIiRZeWomobV2YoMUsqbFT5q6l3EdpnW79jue+63knYZfGQ1n7GaO5yrsa5i8BHvPbe3V3gf6IdA13v6PjOfO+aLirffuuTi8ozvfvw597cbckpYDfNKc79p2ayvOX/yFovlkvl8SUrwyeev5b1jEMfNWurZgqqese0827bn+vYeZWfkxfuMRKu6CjjTY0IsDoqTFFSu0MpgtEMXNKOZVTgnHBKFIoaiPF0cgpRTESRU9L1wVpyzhxQRUjZsrUMbLRouOeOKE5kKAizKDTLmXSfCmbNmLinMcb9ThxR1VTnRRDkiFaWcGHwsqX/Dzvfc79svX6y/i/1YOSiTIFKxY92EN3/++94UVE7ITYv5Ej/0bNpN0TFJJarROGepnGVe5MrryhFDxA89TWXxtWUxr2hbSzYZa2Exr0WczXusFmGeXKTxAcgCycpnmAmtGPPdU+ljgbYpcLRRelJIlFsQQlaKkevr19T1jN1ug7UrrK2pGzcpMh7u26LMskSpEWGjJlC+5FoCI7/9gWVQo8hAipCSoEYpoeOxvsDxHyDOzZiT1RpGATey8CaQ1xxgTvnTkdJygHLl/1SBxxXICWU0YyVG9kflo29cvy5VDUP0dG3LJ59f0bUdPgQwDZfP38FUNTFnTs9OmDU1Lz7/iM39HZeXFzx7/jbvvPsVuiGx+/yGrGeYquLzqx2DD2y6SBcgKUvfB3z09OEKbSSyNkYzbxrRvTAi3BZiZuh3EqXvFPfrDavlnHa/47MXr9jvB9q2l03T1EVPQnNyco5V4EOgb1va/ZY+RGJSzGYRc7QrTT5fQU6O17Q4fId0WVLTfwlRj5JRzIIUyp6bBIVQRYKdklpLmZFlhVfTfYqzYkjJTFLdOYsjqfIYsRaftIil5VF8K4by2cINsQZSQTBjcYzfyOYd7iWP5aJHMoRKCRG5b7HGStXIQpyFnHW5J4H1dKmAGYaO3W7LZrummS8wQ0+Iga4fWG+2LBcL0piCUqUCiIzOWQ7wvuPu7parq9dY63DO8v57XymS8cWRKymnseVAZgQyi4BduYHS1ULk6N8QekYpdFWTDfTFsYlKRAa1qzg5O6NuaupZTQiZbuhp+8Tdfcfrq3uub+7ZbHb03k+EXKUyi3nF2dlKiKHG0PaCiKTiBHZ9IEaoXMX5+YInT+fM5xatM1XVUM0iOWZcJU6T1oZmNsdVDdpV2EoUZGe1wxmNj/mAakVFikY0YGJkvdtgrGK+OGwzs1qznGvsEFA5E4wl40ipRqNLmwNBJlzlRLm17CGmpHWMcwUtK8igAu2sOPYlR2SM7GNGqaJAq4pkAlhTVJDLWlelsCLlRF1ZUtLlNUzcowcS/MieGiMFiYmivTVyz5QmhB88vQM/Rg7KMYFutC+Lun5cTKGoqgaUZrFY0bU77kIgBonQhCimcc5QVZamqWgqkXSW9Iom1ZYYHItZxaY2hCzCVPN5zTAEutYeyLRFJXPcTGMUtGQ618dILx2cFFGXVQdCW+ktYY09wIXlvW6ur5nPl2x3W5bLGdZq6tqJfsWRZ66UEQdFCUkTAhBBx8IRCAcHYLJxN4wFOZG8sYpaBNFkZU0EvJE0OOZvVFZykmkDxqBMSc2E0jjjiNOipl4cD/RtJwdlrMWTaElCozFFMYo6HZybw2iPYk0+BHZty2efXzEMXqDrec3Fs1NxUMicnJ5QWc2rzz9ns15zcXHJ02dv8dY7H/Cd73zEq1e3YBqM1ry42jKEwLaL9AEShm4Y2O972tuWlKGpKqrKcXaai/6FYfCZISS6dksMPXfaM59VOGPwQ8ft7RXDEPFD5OzsnMZVdH3A2czFkydSubO7Z9e23N+vGUImonFPIoYvtwfOiSrjMsZxo4OcizMzHewF0ctFfConRnXLlMWBSXlMCwIxk1IPzhWSZ5m7SqTCx2qHMRU3zU0lKILWWt6vpEQkxaQOEHwW1COnNAn/fRkyJORejtwTVZADz36/xWiD1RZb1WhjS9XPiCoJghKTCJnt91u22zXL5QnGaHb7Hf3g2e5auosnhTxfkmFZdEGyEvXp9fqeTz/9hG9/+99ycXHByckp7737Ps6NR4egj3okcaqxyk1QKa2PRclKWXgh/D50UDS6rslIe4sEJGWwzYxKKVanJ9RNjTIa+kDcRNoucXvfcnWz4fXVLf3QC5m3rkEZrIH5rOb0ZFGA4MxmMzD0HSaLKFrXR8jSN+zsbME7b58wm1m0yri6oW4yOidcbYnRo42mmS2wdY12FaYSouqscYSgqVMikolkvNIErSBpklJsux5rDfOj225qxelS4wZJF3bGEanwUZRyrdQskxVYJw5KGjzkhNZCFnbOQgzkIFVpkHFOVlEs+5gpFZZGq1JJVbYuwFiD0gbr6iJkKcThGBPKGnLW0zxXqlQDWT0RrUH2tRDGfm3S28cnGCskwwPhye/ffmwclC+zN52WL1Oi/P1sWlvqZsZP/ORPc3KyZLe9YTaXct+qSJRX1uCMEcKUysybCpUtuakgi3rnV99/h/m85uX1fWnmp7BZU9WOmBLD4LGuRJNaoZIiREFQbCGziWbBoRIgKcR3gIMjkopYk6IIxEnTrmHouL27ZraYc33zgidPlsxmlroyxPgwxSOS2WNcqaCQy9Clrnfa9Q5YhJAFE2SPxIcDOQfiEIghlWZZIuHfzBxWafCDHFahRIBaIWJIhlGTgigqktoWPow2ZJMESSlRdPKyERtbKiyKk1L4iyJqZKwccLaCbOQzjwIOBTy5PGfmIp9++hmvrq755PUtrmp4+uwJtnbYyvLB+x/w5MkTlssZKXoykbOzU77+tW8ymy/4+ONXdF7jZue0m4HeR9pB4PA+QBcyXZBeLbvBs9luiDFxslySYk1XlSZuiHMSYiT0Uh0WVWboev7l7neQbbGnrmbU9ZykKyIWHzxDCHz+6pqmclycnFCFjO0GdpsdXX/o5fG72ai9MQ51LvSVbCDmBKkQFQvqpXJG54jOYw3OOI/UBGsrhYjYISmWGAJ9itBIw0trLFkbhqGHnElEMJJvj6UCavCiwGxKczvtDDkmWStRTb/TWmOVFQkAH76QpRob+eVchNRyKrSvQ4n7dnPP0He0w56maTg/P6eqKuazWQkmEkMv2kO7/Zbdfsdmc4dSmvXmnrH8v64rZrMGYxVjFUaMYUJN/vVv/Cvu7++5vb3l+fO3ef78bZqCpMnhNKbDDiu0wIlyMHJAEqWaKZfsp3qQnnNVxdvvf52cA/d3ona9226JvifnyGbf4VOibmbsW8/rqzWffHbFdz78mPVWnGwUpS+RQyEEYmsgE9l3PV3n8TGTsHT9QAiJiKRM6tpycjrn8skZIWr6IXFydolrPP2uodKRPiWMq6mdoR0i22EjaRWtgERVGWb1nCEE2kG0Wobeszo/QZuaFM/I1A/G+9XtwOv7PStnpbdUZQBd0LcsPZxKmrkNPUpFdus1KQaplDIWW1WTozvK3FPmcirrJCeoa8di1hBDIMWAKTL5yugSSLppvxQJ/lTK15EgTGtc5SAHctqLM5PSUXPX4x1L5kQqjrj3Pzj/BH7MHJQvQ0uO2f8/Ts4JQFaSr728fIb3PbPFgqqW+n5rZeHZAgOOEX1lS7UNRqLMnLg4PyXlyHbfScmZFgjQWkP0SWSYy2ScPnuMOsfKBq2+3BEsm9cUFRYHRU8VATLB2zaxb3fsdmtCGDC2dOnMX6zimUqdgS+oepV9T1CJwk8Zv0+6LGR535wy0WeGzgs/gERVO9EiCZkcE3lIAtladVjtqVxXiVZHBCTlUUArkZWIMIUglEBtSwmmPpS+phLFSeq+lFejJ39qMgXz2Yx5lXjx3U+4vr5ls+9ZmgY3W1KqW1mtTri4OMc5i89eFC7rhrfffoe296zXe0K2KN0QsjRv6wfhD4VI+cr4lPAx0vU90XuaqpKy2SAKr7n8PsREDF6cMaUYUuT+vkWbjKszK9XgGkvMmpDkvUmRbRDP1dhTbFXjqhmojpD8m9mOMne+LC2rxokoY1rGQL7N08xQE5pyKEAeXzG+4/jf0stG3CvZnBHyrmUSLpwQm5ymMteUBY4ZSYbamGmOp5zJ4RAomdJBdnTav0xUMOfS+A0hvqqc0SUlMT6LYejYbddkEl3TUNcVOUfpPxMRh3EY6LqOvu8Zhp59KxKm+90OV9Ws6vnU4Veup5CLY2S9XnN1dcV3v/tdvJeSdOcqlqsTzNT5l8M9PHA4DgTf0aGlpKt0Sf28adoYFidn5OTpvaf3AaVaMhL4DIMHrdBWkLj1Zs/d/Zab2zU+emIqJcTqoOVCcUhjCvIs+p6cpODYx0yIgtIoY7DOUBVnbb1N+JBwdUPWDpLHZE+ixxiHrSr2+4EhSNNFZ0tjRKNpmgp6xeAPaeSqMrjK0c5qUnIP7n/XRfrgiTNHbQ1N2e8k7VaaTGqNNhmiBFj321bet2iWWBemgE0XNFeZ0hwwq0IMTsyDiM8NnXSvd6WwivLctCpOhEoT6Dw5HwgiXyUt/df8cHBQJidGTWl/pShVjlIcER8RlKNDsByUtnQ5hS/b4H5/W86Z3b7Huorl6pynMfATP/FTxNSTci9y8UbhipZBZTXOZCmv1MIEt0YITB+8+4zz06VolMSI1qKVok0BJoCua+mHURXwOHE+OgGGGH3hpshmN5INc5afWVv6RKhM01TCP8mRGGUD2W7u+eST7/LuO8/Y7t5m3tgvOCfQofJL2fTGpoUgnBQeRmY5i95FCgX+TIVrEKSDcvaZ7eaely9fMSQRmPra+++xms/Q7SBpIBTKGHQtWhV5gFSkm5OxJJQ0HEuRfhgYKxoKzYWh9ZKjnhtcpVksRuQqQczkBKEL5IxUEiiLdbGI6B1M8vuRjz76mFfXtyxWJ1w8ec67H3yNly8+5eXLT7n/4J7T0wXtfosi8fbb75Ky4X7b4qOiT1Y2RB/poyo6qo6YFYPv8UE2ZqWlPb1CEVNiu9kQfM1iMTscaMZgU2LnAyFFhiKx7dyKmAe2+y2ZjpgMbdvjrGExdyKiNW/Q1tL2A/PFkvPzc7Afw80dxjxM8IyKlF+2eiftmmnMxVEY/4cS5MoaRdNYkWjXTDnzfFyqPlVBgBrRjZSmzxn/e8y9xxhRiGiXrkTnous6cfb8IPwvI/M3m1gQuoz3AyDOv1KKurKobDnu8xpSxodE0vJMg0pF6VSRs8ZWMwY/sF7f8L3vfZuYEm+/+z6nZ+d84yd+CnIi+p6r1y+5vXnN7e0Nu92G27vbci/w9MkzvvKVb3CyWsha1KqkV3rW9/d861v/lKur17x8+ZKvfe3rfPObP8PXv/ETXFxckoFh8JNDMDpctqS3RsEuzahjpEpaTSpHdBhf8WAwCX7AaJg1DfrkhLmxBO+JMbDrdvTrPffrlqvrNb/52x9yc7Nl2/boQiqPRcp91/pyyAdCkEociToEcc0ZQtYkDbaWkmhTGVAVITSsNxu2u4HNoIkJrJtRuQWrmUWZhNKRhV7QJMXZxTnOany3JsfEEDP7/cD6xmO043RuOV02aKt5OfTElGhcnpwUK7dOt+sZVKB3UipMltS2tqJIq7TFx1Zkp5RBWY1PgVBSK2Nq2BT1WpUE3TXaTD2jdm1P2/eTsOBYOnx8NIoWUsQai7GHtTiiZXnbT2Oe83HQWt5H5WkvVtM5kYWb83uwHysHBR6iKF+GnPw4OSohRJkQ2mJdzXyxxAeNDxlbKheskcoFZ2QDMaV19vhzoxWLWQ1klssZvQ8YrQhjeW0hWEE6cv5GpU3gaBKOE1brEUmR65RxKOWh42av8lRSmEW8hBA8u92WfSvtyWM8weo3xitHctwjXY3jEV9DUivKjDrlmhyjqCAOnhQiuuTpVaI0UAzsdztu7+9JRb2x7wcaa7FBhMOs1sJ1yLnkEUqKJmViFjJf7yNDyHR9nA65nKRj7NAXfouCKoK1BUECiIdrSVkUMpVKZAtvbN+ycYTIMAzEEJk1DU1TC1GyqVkul1SVK9B7RJOpqhofVEFEND5KA7R+iFICOHE2mEiD6U0HNAtHaOwEDXJIS+fk8uSLfoJSwnkimVLSHNFdL5GWNZwsa6qqoq7kQBz6XualtTRNw3w+Rx85KKrAb2qMzsa03jgVjtMJ08/k/0a5KV3m+9TL5jj19wWmzwENeFMI7jjQGfVVsk4FQRQIXitFmrhYQsYll7UyIS/ysxHFU28cEFBSTGlcQ1mUUvMoMqdKmlE4LtutdAmv6poQA0+fPifnRBg6Npt7trst/dAzeM9+vyNnpMdWSjIO1pYgQtZuu9+z2Wy4vn5dUkGKxWLBs2fPWCwWE8F95M9Mor9Hz674hgATLwXF1Gvry7bglBL73RbnFCl6jIKmckStiNHQeUlFbvct6/WOu7s1u7aXLsswNbpWalxHCKcmSdM+Wd5j+e5YPi2pmbFjdQiRrgv0XWQYIjGICF42VtwtLR3DZU8c+59ZUJqE6DcFnxiGxNBH6pnBOUOKgs5OSMODOSccpZwVMUFQ0vgzpyhBgNaCqxYxQHFCDsT9ab8Z0b1xQU8r5dBeIJMP0hHjhRzBiAmZAzEK0qvTiKmV+ZxHJQRJ9Y/znJJy1aUbchqlJgpfS43z/vdgP1YOyhj1iNiYnoicb0ZlPw6WEU2LkCI5ZGLS1M0CExU2ZMgBlRNNbaitZjlvaCpLbRWVUdSVKXn6zGpeUTnN86fntP1AO/ZmMIq6thjnhD+hwA+dPOfjQyEnUpLqoZwT3osHbcpmlov3rbWUHPswUDcVOWfq2uGcYaz6ubq+5sWLl3z26WdcnJ6wmM0eHCM5DsT2lqFt6XZ7ySuHSC6S/PNlXbQPKnzn6duethXNA+uES3C2WjH0nk8/fc3nL6/5zkefcH5xzsnJCeu7G/LQstIZp6C2BjDkfSYpR9ZWnpqCdu/pPdxvB+n14YXxn1IuqqKF86BgPwSsNezbTFNr5jONySKt7v2Yxx0jfn0kFib7zWazYU9H0zScnkI9c1RVxeb2iq999X3++B//BU5OaprGokn4IRCDJiTNYCr2MbDbS3M17wOoAs/6KN2dfSSHADEQho6haxEUrAhAFf6RtYambugHKR2PKRJixMeI1oZKyallraNvB/brHafLOWre8Pzya5ycLDBG0bdbrl5+SjtvGNoFq8VMmqnlmol+Uw72fJQu/PKVUPbkdHidBsGHjMHZTOUURgtiMhJodeGfWC3P25YxQ0np/HFKc7SDNpC0q+9ilPJ9O0qIq6LQG0uaQg7KByrLWdRKRzJv8MODz/Axk32adFWMLtnJEIvDpcnaoqxlGHq22w2b7YbFckmIQao0lDgbfbdnt9vSdS23t7cArFanpJxoZjMU0HeypmPwfPvb3+b161d8/Mn3cM7x1a9+lQ8++Arvvvs+zWw2PetM4U8VZGSMxDVFoLDsLbqUIqcJNCgO2mHoAOi7lu/81j+nqS3NrGE5m7GczUg6k5NC6RXbXce3P/yMzz6/5tPPr4lZk5XBD5GcZayNVhAClTOcrBaSNnGWwXf4MFBVFm00MRlizvgslXUpZm6u74nDQD9oYtKoqkYpRd/3pKAhBurK0dQOrRxKa3bbXtLj2530CuoT/a6nbT31wlA3lpcvrxm8rEdtHh65WlusrSE7mbhpIKVM8BGbKypXCfKrMjPnBM0t88YpSbWhNTEKb2l0SoweKyclRV07M6GROcvzqisnmjaUxol+KBVWuaRpDEM/EFORj9DSzTjGJNWDY/o0y9hXti7pxzDdw7h/PJB9+AHsx8ZBGRe9954Y41QxMv4OjqKf/8j2A6M2X7jGL//7EdFI6WH1zAH6LuV/RuOswRVOirWayhpGskNdCLCrxRxjDX63R5tYIpLRO5a8YjIWpUppL+OzPUSKWgucOCIqo8jVcX6afChTruv64EQqUTPc7/fc3t6y3e7RSn/hoMgpkWMUDY0YSEE0D7LK9P0eYzSVc6QQCUPEDwMhBrLqUErTtwNdO/Dp569Yb3aAdF2tjIHkiYOiz5GgIGuDdg6DQjuJlFM5vMIQCB6Clw1CSAsF2ixR/OGiNSmK86ZKJGmVOCihpIPI0im3ekA4E9u3LSruSu+NmspJBZfvdugcmTWO5WJG0zjur67o2w4flfA/VJ42MCGtSSVTKjnyGIVjEAv0O4rzydQ7cGxG/QMQxCVOJEk1NTcTITGmcFYpOZSHYWC/3WGNYrlsJPpWihgi7b7DVBltI6laHcLyLzF1PI+mdVB+kcf5qtEqyuGuiihaOYhUIfeIxsgBRRnRmTFhON7X1IjveA0rptL36XnFWFQ/pdw3KUVMgvCJWKI08RGulox3LuTBFMODe4wpkUMkZTn4nUGiWS0Kn2ghNNqqkSZ5StF3HUrD3e11WeOS4kgplNvR072OUvUxBrq+Y7vdELxIzF9fX3F7e0MInrqpOT09Yz5fYJ18Tjxai7LeSyRd3n2ct3pETQqCMkbx5mhPeYB85UQcBqJORKvpVEalgFaSpveDCFPe3WzYbPYiEKyPeD6plNCi0DlisqbSmcYpVnNpojmEhCmKtkOZ38elz8MwsN+D1o1UQVqRnB+yrNkYg5QOBxExozzDmGNZW/KcddljXWVwlWG48ey7QUrf9RsLWydJiSSLiGiOCJT8G0OcUuVYe4RYHaNvxTksncrJTOtx2sMLcgRKeDhjIz8OlW8F60CpUmGWIinHUkk5jrfw+CJxfDmU9xhbJ4wyEyOyk8YSs99D8uLHxkEB6LqO7XaLMUIUPTs7wxgzISkPiGm/V/tSB+d3i/D+w9m4ecYgUWwIgRDlS+U49Z6wRhCFprI0laF2hnljSUFKyRpbkdEMTy/Zti1774vIjlThAtMzVUqaFPqhfHYau+KJZLioScYCR0YOnTmhAIQopei6TnRWCtpVVU4qflPm5uaGjz76Lm8/f7sgAfH4pkUtMzF9EWG/3tH3PevdTrgnCpHxNtKRVCvYdHsGH9jvIrt9xyefvWYxb7i4WLGaNZzNGmz0pHbgrt1DyuisqecNJ0/OqG2idorBDwx9pN9HhiETBxHL0ilPm4EukvujFIwuB8QwBLxXdG0Uh9EeuqiqlLEu43wWQunhprm6viIM9zRNzWK+wJkl2+2Ol69eEPun6NRzcfKMxXLBh7/126zXO7JaEJWmT4k+pLKJimMXY18E+0Lp6bTHFw2d8dAdN8GxJPZAbBYOxtAHQE1zQ3bDojOCElRBG4If2PmBj7/3KScnC772tXdROTGr54Rh4H6/YUj3JBQX755SzervdwWMyUVJE6pD3w+rE1ZlnE4izpniga4ywdqFWjj+YnzkZVMXLZE09YaZOCoILymlWBzkBMlgbSlNtoYYIYQDkTBnh1KUNSRzwftE224hxwc+We8jqQuiequhcbZ03E3F+daYesH85JJ6vsB1e+7ub+mHjk++9ztFVsAym0ka0BiLtYWgi6CWWkHb7okpst1uhEzbdXz44bdZr9fEFGmahrffeZez8wtJC+XC3yp7oNaHsR9ThHlygnRpI1AORckHE3WSvjy8sV1mUDGRfSCYgW6/4yZ4FvMlztXsWsXt3Y6PPnzBeteKgJnRGKeFhJzAaRHGU8lTEWj0wMms4tnljMGDD5rBZ0LIbIYoKdbMJIzXti0x7Dk7vaSpHPXcgdLsWhHaC6HHEAk5YpB+PT57AqmI5WlpxkfCKc9yVbNYONq95/6+ZbmsUCaOiUmZaiaA6cBbKUfOo+6QOBtD15cCQYW1VdlXVOm/NKY9QRXF2VSqB42rynknc9coDVmJE6VFHVjUvUvpfHFsUo4kOnwQnaMURdbQGSspXGuJStLr5DztBSln/CAk21Qk7YVYLt/nLwz492c/Fg5KSokQAr/5m7/Jb/3Wb1HXNfP5nD/+x/84q9UKV7z/H5b9oKjN932lRTk1pp4Qe2IcSKnUyCuJKHQRyqmcpakd83lNZRVVpfHJk0PEIjDgybxCqURljSjOJikzG7x4v8nIpM4pTXCiM1YW0KjIlCFrTVJjbl7hrEzusRGWVlJ3r7TAp7Lxm+J7KNpdy93NHev1PXXlvoCgCHScMVpaqyeDCNFpTUyRwQ/s2j19LwvIWYMxil3b0fWeTz67J4aEcQZXW6ra0vUd17d37KzC6Izv+1KuqnFDz13sOTlZsOoXVNrhlKHWGW0RqLlEYpPstxI11n5IRfp61DMYeVGjAN14YirZHNLYz+SNoTYi7x2zIoTMfr+BnHj69ILFzKFiz/rminZzz27T0ncBt6yJUTF0ntAHkveEYZjSCxPnBEGfRg2DmDJHKiLEsr5krKCyIhmutMYaiXBjTCVi1qUH0ahUmbBGmrNt9qJvsdt34iTPl3SqxQfhH7SD5+R5pDq+72OvgQMfRAK38XejFuxBME9QqiwIipaoUCs4pCKODskxXz6eHGpEOvQUBAi3aux3IumjGDMxjw6bLyXBY3pOUnWRXEo1IykVB6cQUqeo8o3BDlGqLjSS0klDK6RbW2OtJWdHokLZBcbNMa4BBDHp2h0hSHl+KTsrKcRYHB5dgjbP3e01VdNQVQ1919P3PW3bkmLk2bO3ePL0GcvVCSlnbm6u6YeeEHyJjjWLxZymabg4PyeXKqdxvKbIXR34PBkmdes3t2OtNFVV4SotqqSFo4O2RDTXt3dcX69p9x3EzNliLq0rdCQbIZs7paXDRMgs54b3375gsZhxejqjG6AfFJtNDyng+45uCNx3HmNFrsFZw2wmCHEMLf0+g7Y4XaEMqMpSaXAqYDCoPKq5gp3JGOQQJyVZ50ZkQ0/VWwVYmswPGR8TFuFKNfMFOSW63a2grJXB2hpjRT5CkeUZKdCFFDx4SeeIPpPslSlTKnEEvUgKRhmg0amfGkuOc7+sr5GMTR4lIg7Vll3XCheu74t6rJkwyEJHYYRA00iHmW76/6ApnjHK+ef//J/zK7/yKyyXSy4uLvjDf/gP0zQNVVVNi+R/r6Py5Y84/26//L5tCuzeuMaxTO8Ln5pEqCzGjhg7fOzJycNEsDKTY1A5yZ0ul7U0TNNJKlJykqZpWnO6qNFaDiBTNpwQpfEWgC5aIyknQkhYIx61KMFncpQGakZL23dKFFq5g4NitUVrSyp56bbrAHC2Zjw2dts9KsPd7R1NVRWU5vCM5HBPGC0S1FlLT5NYOTKJ7R5u7+/oSmlh5SzGaLpWBMi+/Z3PsM7yjZ94TtVYXGXZtR3ruz1OgiZCjAVqFUKc3Sie7Jc8aVc8P79g0cyYWbBHR92UDtMZlPS8UIVAiorTQhX10Sywf4nIUQI3H8iqb0wmo8nWlpRSYHN3z8lqwXvvPmO1qNGp5/bVS1LMbNc7hqgxJzUpJvp9K6nPYZgcFFRJNWSpMEHJXPAhSFuCcWNBSLIheLquQykl6bNCirXOoZMBpFpJ0jxCKMxRdE3GMtb1tmPwkc12j14uuDw5J2fNvhvYtQO3mx3vxMNYT9lD+U7G/0vStAeYWx/xS4SUaVSJ2NVI7JZrPLT8g0lSk/F95F9jjKRgpoqeUnWjpJuxNIUT8bwUI7E4KM5UwnOwRTq+SNyndNARmkLW4hAd7x0hSXddlT0qR4a4w2hDVS9wVU1EkZVDV0tsPZcy7Szpsv1ui3UK782UwkpRiyJzkaO3RUr++voldTOnqmf0vWcYPG2RI3/77fe4fPKU09MzQoi8ev2KzWYtjQdzwhjDkydPOD055ez0VO4xj0yDh6q/h3VBEQyLDxVyEaetqWdYp0vDTwPKgHFENK+ubnj98o5212Gt5eJkRcoDMbZyiiWNUXKApz5zurB89f2n1I2jaixtB10PYfBEn/Ddnv2u52a9oZkvcFWFczWLuWgIeT/QDy3GOE7OnuCcpq4sJkdMiqgUUUkxBAnEmoXozwzdHouiUg5byTrX2mKMK/vkw2U9DJm2y8zqjHIwW52Qo2e/fYk2irqBqq6wbkX00kG5qWWPldR2JgRxTnSBA5U+EODjNLdzIfcLC+sA+h/OLknJQM5mcjbUiO0VjmAfJB08DAPOVdR1U1bRGKSNzr2kgA+Zi/8Dlxl779ntdtze3vLq1StevHjBcrnkn/2zf8ZXv/pVfu7nfm7yYA9h0sPI7Nje/Mm4Qf6Hoq9Mjgkcoqljh2qcOW9eV+ET+OAJcSClgRh6wtDi5nOMqUQLxRpp5FZXLOcLhqFjs93Tbff0bcuTpsHVjqquiVpRVxXWCBkqRUkhGT1C03KgWiuy+ScnS0LoGfqWaSGMMG+p0JjNZsKvKC51jB5t7dRsKuUsIldoFIa+6zBa0XYtbdcd+BDl8eSQGVrPfr1n33kGn+iLVsN6u6HvB3IcRF0xZSlVDLBeb9jvB5yRJlomBfrtjlf7jqZqqG1NLpU1upQwizNnqI2j0nMMc7KeEU2DXmiqpDB+vLA01RfHJL1lap0JZFIUYm0C8YBKO3UVAyiLQlGphLVQV+ag5TCNtehT7HYdKWWRLXcV7TBgnOPk/JTr13fs9x31bIVOmnYnDfuG4pTEOKY5pB9TzCK2NqZvrLVUVUXMUnI9zsmR1yTVPHHiHWl9SIUopLJEaQUBDMUhMEKsc8aAUcxmFavzU1bLOcvVHFQixIGvzL/K2wkWi8XxXT9AUI65ZEcrhtHTUEozljqMTQBFsp7SJPDANxEUpah1FEXMGOXgNGOfKYrKZhq5KJGcbeFSlLRFFlE3X5w/pRVzCkoyqmwquZOUpF+OqNMqnLMsl0uCHxj2ByGrlCHk0vsnZeLQoXLEt3fYakY1W6JtDdpg6xnVfEFVN0DCD3tEvzbS7lpiyGN/QlLyaOtIKYhAou+xrkahGYaBvu9ZnZzgXMXFxROMNnz44XfY7XZsNxvadocfBgY/YIzhK1/5Cs+ePeett94Sroxzx0M38RtGH4x8UB5987BWIEhXDgyDONBGK5aLFcbWJC8l0LPKoBXEbkeMA963+ELW7nuPUpknFzOq5Yzz5xfEFOi6HVFllLXMVwuUrbh4GqkWHb1VVFXNvKlLEYHG1JU4obYGpYmhR3RAFPPKsqgrtptIPwSMdWhjaSpRwVVEnLZUumKz27Nve5QSYnllQZua4xOmqhcoY0RrSBsGv4fkqRuLUQFCi5stmM9AVxGVPCruIURcTlRKM184gpIvHwwhCilYEF3xPJQS8TqUoM56bNuRIRQRtbE5IcpMvdNM0TQha+kHVda7dKyWvlVCqFWEEKe9QqGE51juNTw4c79/+7FwUGKMdF3HbrdjvV6z3+9Zr9d8+OGHWGv5g3/oD0oeTo/Fhw83vn+Xja/8QqpmzF1/f2/zpe+rDiHig5BRFb4M479vut55lIuW6CwWQlOIQdjYqWFsWW60LmiHoaoqYf7vWvq2x/cDWSts5bB1xRCzSNGXA3TkR0xEqjE/X0Ti6qaGLjL0BTgsGKJSlM81JeIWRyceH3DFQRn7N4ycgNFDH/qhyFenB/edE8QhMrQDfTfQh8C+Hxh8YLvb4r0v+gnlQC1RRNt20htGCVmPGPE+sPMRvVJUczeNS2lhgdWaShsaU1GpCqNqsqpIWhpkkcGYJOTLUlGVg2hkxAhOSzTvRyIskFUij0TBnEorHxHHc0a6OEuLgaPbztKBeugHFIrlyRzjLCEmjLPMF3Nev76j9x5TrcjRsNsFBn8gwB74QoKWjHoIuXBntDY45xhCmPha09TOhyq56ZAZA3+lULZEUOPhw6GkVBxVg0ZT1xX1vKGeNzSzmhA9s6FmPluiqoaudzzI6OWjlTqmCvIYfR8clAeCZ0d+i6R1Dl8czWGFmuThxxJMVJHFV+rI+ToSrBpbPuiyj2QpCR2rfpSCuq7ReSSl5rJ0H5Zw68I6rJsGpTLD/sEtT+lOsiLHCLHHhx029MQUcc0J2s1QRuTurXOEIFwQSESlGPpByLjleVp7kAJI6eCcUtac957ZbE4zmzGbLRh8z6sXL7hf33N3e8Mw9ATvGfyAtY7FYkFdN1K1NGrGlOeejva1Y1k84TZl9EP/G5A0qCKRo0cbizaWylXYSj4jDB5rxWkLQyfNBIeOIUR8TGz2O7SGi6dzdG1plnPhVu1EAFAbQz2rUcaxPOnBGpbeY01FXdmiuq1E7NJqXD0nJ8XmviWHRBoSumqoXcUOL2mzqkI56YuUUaRSiOCsw9/v2e09owaUMaCVfXBWWCtdmp3VGKWIUVJQzmlUghw6dA44k6lMRGfPsNuSo8foJFVArmEg0WdI0RARhW5JS4pDbLSDgnRLyomHzTmLIznyzMjxKCVX9v6spnPIWlPUwDnSwwHJK8mecOhKncfegz+w/Vg4KH3fc3t7y3q9Zrfb0bYtXdfx//21f8zrm9f8kZ/9Q5yfnXJ5dlY2qIODMsZUsm8c1E5HvQI4dBSNR/CzzhqNISchK4WiHjql2lIqae3DqKSj+vNMJirQKeFiwsaETUk6rSpQszlYC1UtTorVX0BRciqt8qQkAOsM7T5wd3+P05LiOVlUpYQSiIG72zu++/En/NP/7V/y9OKUi7NT6uWS0ycXDCFgfJTJV0omZU/P0ybkKkfOWfplBM8tkZxCIV9KQzZTotnKlg7KWmGUwWiN96I3YI3wUOZGynYTQvBKQRZVjIHrm2tAPnuy8t62qpjN5uyHjhj2bDf3tO3A66sd7b7n5nZXdEoSq5WU3+63HV074L2QG1+/UjR1zWK2ZL/3tN0di7mlqS3vPblgXleczVc442hshbMNzlboLktJbiXyz6Jop8CArirQoItuSB3SJJ2ecyYkqdQJOZN1IKtU+rRY3KzCVDXNssLVDzvm9XevGbavee/iGc5VOGeYzRsuLk94+9klpxeXzG9a2mDodwafSoVBTCKgVyp1RqG9mEKpMpFDWXgJMr+mnknWCkpXVClHDoq1mrpyuMrR9QMpRmazWYmiAjkmku+mk7ZymqZ2VJWlqh2b3YaZU6wWFzgzp7KZ2eVz7GLFRx/dsN8dym6ls1RJEUyb4Hj4xennwu8RYfWcZclopcTZSJmcIqk4naOI23hwBuLUoslgMUU7X3weeW0Mg0Tj7QajFPPaTWsDJUiLpiAjlZSgjg6POOUH1U2FLgiZQuWIOj6xOMIglaBOev6E7DvCLjG0O+LdC9zsFOPmdO2GGAPGWVxwhMpiVKYy8rm9b6UMH2mcp5XoaljnWJ2coZWl7zuGvsMPPc+fPaeuG15fveL+7pbvfPu36bqWdr/HOIe1jmfPn3N6esrXv/4TPHn6pNyvEoRo0ugYb6YQkfXhZ0ZBNfbuGZe1yhidcM5Qz1YkJD3x0Ye/w/3dnk+++yk3N2tu7rd4H+m9Z/Cetvf4lIhZOEdVZbjYDzQ3G37733xIpcGqxHy5pJ7NCEvF4BPrfUDVDe/NVzLHQ6KqwVjP87efs1wt6L3DB1Bm4Or1K/63f/GvmdcVi1nDk8u3WZ2c8uxiiXOGdvdK+s2EhDcV3tXUzQWX7oxheE2IHTEOgs8d/DVM9rg8UGvRMGqHnpwDOgW6/Z719Wv2vWY7aM6rSKMj3V4adOqCAhrVYZ1jVldYe0K0DVul6YfAy6vPcbbmyeU7wkfzklbSaO53r4jJc746x1knqfeCJDazGcu6xvtegpuCjoyKsNYeeJ0HonQkZ0m1jYjh0UTg92I/Fg6K957NRpjooeTIlFK8ePmC1cmK169fQgrMjEYpif6iGh2RIoA9OiiTWFV66KCMB2V5zgaLxUmJaFYMShQUx2oNgghZjVC5EAnjwQEi48mYmKh9oImJJiUqJV1/zcqjXIVaZJSzaF0dQqFio78yEpHMRFiM+AI75+K8jD09un3Ler3h9dU1i3nDuda4qhKhp7Gc8gitGZ21EYmw1oIqjaTI9H1fBKTkuY1kwokcVgSsKIspxpEkJ/l4nQUqN9oQVSKUW5Ry45a62orc+NE9y6Erm5Icvr70Q5GUTozgh0RIAZ8CzgnJaxgCPoycAOh6j1GOXI/3GUEZtAHnNJUz8mWMiLcVsbtcykC1iePDJ5FJJHIhCxslzHujpBdMNEXpMUPMUg6ZdMmza4vSFjdzGOcwlShFHpuKAZM8M6slutKZeW24PD9hPmsATVaWrETdNlG4LEWbJh/NaVXmwgh1qfG8L1j8iByI/skBi5eU35jmES4GKRYu1DjPD/wPef9D+bkxWkQAvUSftTOkoOm1KLK6kvY7zL3jf0ehelWchhKVTRHBGHSMiEWJ6ka05XitcLxdlr8qrQfUEcoxIkIi1y37St+1InRIM6Uwx1J+VKngsqYgJBmdBRkL4UA0VGr8V00SAQ/GevoPBWiUaYQ/oCti3NJ3W0LKaCuH3tiUU5U+P0IMplT2CfFZYiZ3gOCVwrlKelEFT07i7FknyOB6fct6fcfd3a2kdYae5eoU6xxnZ+dcXFxydn7OcnkypX4neQN1nETP05wa721sWvfQMZOfOWvQVSWHfYxs1mtev7yha1uC94QYGUJgsxfkpB28OP05U1caHTXDEOi7nu16w6x2LIoKtNaaylRoC7N5A9qQrQRFYfBULk3VTlqbcjYoqqYmo7i927Ixltq1GHeCcY04E4CJQt6NWfY4j0NrR+UcrqqK4z5MxNTDXZdUZEH4ZJYXUnXwku7ed6hqzzwLChaDlI/rgsKmHFAq4WzGGUm/1dFK13rfAgmjAyhTyL2y16VUiioKkHKQrZD9VhuNCl9cj3CoiD2IOubp2FCFUyUyE8dr9Ae3HwsH5fb2lt/4jd/g5cuXUipWIpcPv/cR+27H//P/8X/n3SdP+Ol33y0wYsSTCGR8WVOyd6cp95bS2BAlMfayCf7QK8SphlrP2feJLmT2Rkhylatkg44iZpaPiGKjg+JLc7EueSofWOw7nmXFExRnRlFbjb18hlmuWH7la9TnZ6y+8o5E7Uc2Tg1p/FWzXJwS+oHtbMnQD9wMt1yeNixqjc6RMAzcXV9xe/2avu9oFjOevfMWy9MT6tmMru8BcUbi0VeIkeD7SWvFmHGzVVIqbEROfOilEmDeNBjnRF7fWZpK+k/ItSq8GVvDJ3rvBequZwgwHKcxePnyJdvNlrOT+cTJ6PuB11d3bO+v2dy8ZrNfM/gBV805rZesVpZhCDx7a8f17T0vX99we7ujbXtA9nznpCS2j5ncDwS2XF4uODud8/TJkllj6bOn7zw3uw0zW3PeLLlYnXC+1JLSURpST4qKodO0Q+TVuqXznjYMvPNkxdmyYbGoMU5DU5NV6RNirTifbga2RumqEGcVKIMypUvqaAouT1dQD+TQM8QBlGL1zlN+/o/+HC/uWj7+5BUvrjbcb3qUWhSHuif4vjTqGlsP6NGnAjKVc8Sc6YdQeABSTaGNoW3bCT1M+VBOWDwTcgxIJVlkt1mXQ086wZ4sZsRS9p6jZ+gT1ilihug9OidO5zXd9p77q5dc73tSNaftKo6FUNIRnXKUoRqF743S02FYapEm1G+M2HVJ46iHR6ZEeONnGCSVwpiySVMJfT+0hCA9UGKMXL1+JUqeZxcsFotJXTWmhC4tAJq5CJr5YbwO6ZDsfSKGHlCkIGthCJ4UHgq1GaOprJXGh4DWTvpuzS7oh45uyPSbzwi+xWghl+es0ErImEYlrE60YaDrhoKgKNJSRA9D2X+0NrR9y267RWvNbFaRY6AdBj76nX/L/XrNer2eruvd99/ng698lZ/9uf8TF5eXnJ2cPlCWHeX/9VHFinlDDt9ojdOZ+AaCYrRiMZ/R1A5la3ofUT5w9fI1H334Ec44TlYz2iBlvd1aVJzRFqtkr26soTLQr+9pTSTuL0nMoa7o2p4QEpfP32LRNLyXNbu249MX14DG6VMap2ic4pOP7mn3VwxZnJOv/eQHNMsZqppzt2nZrHfcbgdOVh/Tt+/w5HTOVy5PsI0DXbHuAte7O7KpyMbxzpNTYlpxdfWCkI2QqSfTJaiYAxanRU5p6PaEviNHKQPfemguG/TMkqKHHMXByokQPUkHkk4sKsfMZWqtaXTkY62wNrNc7qjcirpe0Xd7gu+4uFgJ+u9nxCgyHbJHavquK9wUQUFHdNUYK6unjHfO+ejvxnU6th0p6Vh1qAr8Qe3HwkEZNTX2+30Ri5GH44eBzWbDR9/5Dv76mtnNPY6EJTHkSCDToaQYryAnE8qRhVegOMhcpximCMzoBmsX3PeRnY/sHSSjmNU1Go0tG7konOpJUyGlJOV6MbL3HfUQWO07grGgLcFAozX5Zo1ZrjhBM+9a9PmSoUyEg42eq5Y8o6lwVcNsNif0HTEIDyPHIg6GEAUXs4bnz59weXnB2dmp9F1RaoKgJ2nziX+Sp59JmaEQZscum0qLUJdMyIcdi60pgnllfvriqaccCxoShIh2xE1JJaffd71UEy0bisgKMcK+S/QDDNGi9AzjLE09lz4pSXRfvPfs9hXWSSt671ORp5aIFqUEFjbgnMj9n6+WnC0WNLVEHykm+iSquiFFEqmIQwkkLXLxolux7zz3245d17LrWlaNoXGKxdygsWRVqhOMpHJTTlL7o0qkogpZBTWJIh0P8xASaYgkhD1/+fSS1dkFzfKEcNNyd7el7QYGn3CucCqKYuSbEY7Wo0ZBLqWAI9IycjPK65Q6IGDTfBujYBnXWWmA1vcDINFnJuIL8bbSktZxTvhKlbPlPctgxkAOnvXdLfu0oZk/R5umfJ568CXR2Pjf+fCzaXaX36kRDJJy7uNn+UDMkMMmShaKox6bU6qjNFH5zKSkHUNSimHoqevqAMkoHhzE8pz1EalYPmx8zmPPqpwO1zLaJCiXDj1rhEPhMLbB1iuGoSXFPWFoC4E6lf2pOJMcBBxHoazxMcSjSoxQtG+qyuGcKxwTL+qpMdHM5jR1w2w+5+133uP5W+9wcnbOfLHC2KqUTBdkM0maJaqDozgiWdNoqvHrS9Japa9MCgO7tme3lz3MWU0zmxMSbIeBCNR1R4gZFSQ9k9MBsVWlomQYIs0sY6wrX9J5WvmBkY0oxH1BW1VVl4pPQ0oiSpdSROmEMdJxWWlPzNANHt3C6+s7wtAzV5m6qqjrBUPI6JhLuX2iahwZIxVg0RyUkst8VllNjrEqaKpoykj6pPOB3u/YNRmbLU6FgpGWma9ESE0bqcrzXvaBoS9aWCTIA84mlnNNZRXeK6KaEZNmvdEF0ZNROkZHHohNjvDYOOUfLqxJDE6NHeYVRSvIT8JwP6j9WDgod3d3vHr1itvb22mTyICPgfXNLb/2q/8fnpmKF27BSYZlhiF5fE5sjCbAlJ4IMaALm9moXGK5XDYsXTaZTKwafL3k877nJnj8wpGdYbVY4rRhbiowGmUN1tmiXyAHwXa7ZRgGNus7Gh+5aAd+Yr5kM1tySqQiswOYLzj9w9/l8utf5Sdnhvubm6O7FpguEzHaolWFVguW84h+mrm9esFu0xH9IM3LyFRWcbqcsTxZ8MFPfo133nuXZ289Y15XQCZ0Ht/20u1y8FMVjyigSiNA3epSfTDHWU1VKk5EllsqF3RBCurKUdVCQBuj1rbPhBQIIUqL+hA4lOnKQZ2SVJ5sd1tCjDy5OGWsD/A+c3eficOcpAzzpfQVWs4kh+19S9f1xODYtY5mVqGMbAoaWcjVTOSdfUgs5ppn5w1fffuC9956xul8jjNmUta82+5KtJ2l8tGW1JRS9CHRpcTrbc/tpuPj1xJx3q3vaFzC6sjFymCdkAKTlrnmfU/f9tgKjAtUsyJqVsmBirIlFTmONLy+a9nfbUjKcPnkkv/zz/48b7/7Dnb1hLvd53z40Qu6aIlZZEdzTCLGlkMhuY1om2wsVeWIUbNvhwLzB8ZMw3iAj3N2cA7S2N9k5JVYFvOGxWKBNpqrqytiDFSuott37Ndbmqammc04PVvRzBqa+UwIhCphlabfb0hdi06Bjz/8Hp9cbfiD/8mf4OSsme5do0mMOq9SHKz1oUh4PNhylh1aa0pDQA0jv0dnRP04TyJSx4fkeLCrPJJplej0KI3REKMtDS0hBk9Iie12g7OGlM5K7j2TkziZo/JnLBBBDKU5JBRUMhGj3Idz9guHtbQj9ofeLcVh1baiXlyidUXOmhQyN7ffY+j2KC1S6VonIomkIj7m0ijuqCggSTVMu9tyd3tDThIxz2YLmqZhu93R7lv6Thzh999/yrvvvc9Xv/4N3n3vAy4un9DMpGeSz0fdiae+LRGTRXtIa4VVo4M5JuiEg2LecMJzynTtQLdPbPY9r69uefX6BlLi7bcuefr8qTQpqGB+39JH6PrAvvW0bcIPSdaQs7imRpma+/1AvdTUixPqWUVVW/b7PWG9IUZN3wf6VoQKwxCYv/uU+WLO6mRAa8t+aLGVRtPjXObk5IS+h8024HNm23t+/TufUxnF986XrGY1b12es5g3LJcLTOzRWbN0NZiKdj5n7zV9f0h4jPMtxYGUAq6aiaOWFRhLM5+zvmm5ul1jw5zNwvJ0JWJ91iScVbhKizDfwtF2A9tdz/1mSz9ETJ5hUfT9jrOTOc8uNTFZYnIM8ZLeV2x390CQ9F4JPGRNpclZiWNwPqV0KOtI1qIauVdG1qFSGmUM3X4nrRRsyWH9gPZj4aCMUtqjABUACgxamiz5IAzunFkkzWlWDFEOC2U1QSlSGhs2SZStlcZqETvLhZmsKVFnTHTRsEtSbqiNwOKqdtR1jTOGSjuRvNYUXgpTykSqTAayD6iQhDQVM9FHPMJrCFZyhYk8aYa8mcUbO4celB8ERWmqGU0zI/oebU1pBS58CruYoasavVhwerKkKVGgNMrqaNuOrh/wXgSZxkohXXgiU0ALU255/Bpz8taV0mbnpLx0hARTnp5BKtUZYzWDCElJrxevD4hTLD0fRvMhs95Ehm6ga/e4KmJNZNcMaCVNviRdZzHaMXONaFV4iSZDgnTXYZ1hMauonKFpNCH2bLb3LKoaZyqMdWSVqGcCaWqVwDi6mEi99JpYtz3dELlZd2xbTxx6FJnaVQXpCby+XjOrLfW8IStNUIbWJ3Z9IustKEOzWGArx2I5l35B9fwLaNngoQ+ap29dcP70EjerwCi6Pk69MywGlQ1xrGAqZei5oHlCMh35QVICaHQga3DGEMvzGVEKa0XV0llpuqZVntwCY6S/R+WE9xCHjhikD4yKnmEYJIIiU89qjDXMmGOMZdZYaXGQM5U1nMxmPDu/IOsZ1VGpqszq8TNVydGrgp8cxKQo6pgaI+6MylMPm9FBSVmYOYwk4amaJU8psMpVOCNlrMboUqVQqpFIZGtYLheQMotmRlW5ySGXNSab+2I+x1Uy99u25fb6lv2+Ramx9FOqx0bhNvKDmJrt/Ss2w40cUkg7CK2lai6XtI+r5zSLM6r6egogxhS1URnpjCn3oeNB2O5QASWRcFVVWFtRNzNsVaFUh3UV77z7AXXTcPHkKReXT7i4fIrSmv1uO6V9tJWqr+VijrOW2llxhC2YpFCISrJGuH4lzivz7yEHJYTI65s7YvCs13t2uxY/+KKcW6FMxhrNe++/w8XTxMmF5/XNPZ+/uCZdi36P0oasDEOAtk/c3HdkvUa7l8wXFXXjOFmtqOsZGYvSnqbeS4qwCBGu1xt635N04vLZBVorbm833N1u6PaS5m5qhzKxEKEjQ4Lb3UA7JAYPs7piNdsxn1c0jZNS8Lqh0VoI50f33W43+O6O84sz6rrGYCB7RpXimKQqs+t7NjtFzo5FZUnZUFeqaJooch/IOtB1icFH9jtRg1XKluq8hpQ1u32LsaB1Q9tlIRkXbk9O+UiDpsyRommy2W5ISbocW2ep6wqDxtiRb5JZb9eknHF1g3VOROcEbj1KsP5g9u/dQfkbf+Nv8A/+wT/gt37rt5jNZvzn//l/zn/73/63/NRP/dT0mq7r+Ct/5a/wd//u36Xve/7Un/pT/Hf/3X/H8+fPf0+fGWOk7/ujNIFsZEZpbAbjPXWCEwxPsuNJMvikCTnRKCM9Vyh0OXWAsCtjimDZkXgUmagia+VAORoHNQZ3ssI0Ncv5Aqs1NYZIJuRDH4+xEiaEQPQBExI2Qa0tOmtSBF+ineiMNJZylmxLlcibsGhKR/k+DVisqXHNAj9foXKUHhpGiLBNI9LjzXLB4uKCej7H1RX9rmXoe3bbPdvtnv2+pR+GKdWVUkJpKYObytCKKQ4OirUG5yyVc1SVo6qlOR8FOQohEnwsXZjlfiQlIGNotMFZi7dWoN5YCH7HDorP3N4FNps9m/U91u0xdqCp1igSfe+Y1TOenT/F6oZlNUNlw9BHghGo++6uYz6rOPtgzqw2zBeaftjy+rrnfHnOrF6i3RJrFXO7QhExKqBUZj8ktvuOvve8ut3S9Z62bfEhE4eEVYrlbElMis1+wO+3VEZxebZCFSXYbRe52we6YcDHQLNcUDcVz956St00zE5OabfbB2PdDdB5zdvvvcvT509wMxG72+x6QlRYW6OSQNO7ThoChiISNzqCI18AEFnsJA3WZDQdvsDl4/hWzmI1+M5KBVuKIiVOwhklbRMqCyiG/Y7oB1ylUaGn63uG4DF9jyviZqvTE6y1LFenzOciqtVUFRcnK77OnPOLTNfMJm6IQvRUCoggOJsuFV9okpC8UFlJVV1BPQ1ROnUbxajAn1Iobs1YVZWnoKYfBpRSnK1OyVZ0TqyR9gtaixCW1MtZLs5OURlmJR2QU8Q4aU2vy/w/O12xWC65vLhks97gbMXd7R1wDyj63tN121ISLOW1x5Tou1ff4+XNGrRobCwXZxhX4ZpGSoqrhmq2xBjL9v4G6cwtCFbKkagUyYxOpibEUqV4lOob4fy6mbNanVIVJ0ibHXVjeef9r3Nycso7770nJPqqZrO+4/b6mpub0qtnuWQ2m/P02VvMmpo0L409FVMXcjMqfh05R4eePId7Hnzg48+vGbqWu5s1RhuscSyWNXVtQUWss/yBn/5Jsmrow4xvf+d7ZP3b7PuWTbsH40AbukECnvx6x3o3cHO/ZrUSPtjP/pE/zHx5gtEOVw0s7rYolQmDqOi+DgNDTihjeOeDd8gp8a1/+r9x9eqe7bol58xs5kr34kTE4lPiajegUs/nr9Y0RjN3jqcXC85P58wrx2q5ZF5dgBm7R8t9r++uuH/9Xc6WX6eZnwGKnAdSHIjR44OnG3raruVuk+gHy7JpiMmSlcEkhY8wJEUXhCAchsB20xJiol4ZrK6o6xUpae7XWxaLFXU9Z72JbPfCU0qjo13Sz4eB0uQUubm9InjPYjFnPpsxnwuZWmnRPwohcnV7xTB45ienzOYL7GwuHEzze0vvwH8AB+Uf/aN/xF/8i3+Rn//5nyeEwF//63+dP/kn/yS/8Ru/MYkw/eW//Jf5h//wH/L3//7f5/T0lF/6pV/iz/7ZP8s/+Sf/5Pf0mVVVcXJyMkUao1lEYOv8fMXKzbDNKUEZ9mhCFk6BrlyBISWHZkrET4a6ctjiJY9cD5DDFOegciSTOdFQPT3FNDWzusEqTYUW4l4Zl4nHkSK73Y4UIioEFlnxNGuem4pnxjFX4LRCnZ1gVktOv/mTrN56xvP33uOzu/vp3grXm6l2QUmZpdFSXeSqmiY1KGPIShGiVMi4yqFtRVU1aGXIUfRB9rs9d5st97sdnQ8MYzlN2dhGDziEQE6JwRnAUlemdG+V67BOeoDMZw1N6QbqQ8T7KAe7jwJvW8OoSEOpnhqlmEugW8pvH07sGKPomex37Pdr5ostSves7ztiVOxbS11FhmEPUXQgVsslz57Cbr+Rbq/aUDcVSs/Q5oSqumTwHe0w8G8/v6G59sxOHChL7z059jBscUbhtGK729H1A9u2lzx1CISY6QdBMoxW7PYtfujY3F5DipyfnVA5QQ+ydkRdMwzS86j392ijCb6jbmoWZ2fc3z1EUE5PV8zrzOXlBavVgv1+z2az53c+/JQXr264X99R1yuMqYU3oUdBpUQIeWoPf8zUTylhbGnSmIU4SZaccQx+irQXiyUpBXzfozQMQ0/l4Oy0pi8ptd32CpUSq/cuWMxXrE4WUg1hLDF6UvYMm1vifsP29hXDxYpvnH5l4kxI1VkiV/mII5vQqS/plwzJkKMhmxqUES5UVlKZQMTkiE5eFFi1R6lEDBmtIzn05ByJ0eOsxTkRGqyLM62VkDTrup40g+rKCXqUK3x0pBRZzWdopWhsNQnbzeZz6lmDrSqcczx76y0Zx/mCuqwBa6xI4xc9HGvdxAMxOlO74zke8EMPiON03+9lb3IVrm6oZ4uSs0uCDDdzvF8Sw4AfuskZ0EqqCqVbuHARVE5opHOzczXOVVjrRE7dOd77yjcw1rJanqKNYr/bsH+9Y7tdc/XqFev1PZv1mhAjzXxO3TScnV9yeXnJu++8y9OnTzg/P8MoyEbQIlPQpZFfrZWkfo5Xtg+ez1+9xhrN7OSEJ0+f8/Tpc+qmxjqLqRQhRV5eb1lvt3z6+T2ffvaSzz7/lN1e0rAxSuolp4DXipyszKsQaLuBZuOYzb7H2es1Ty6foJRmPptjlEGnROUslTP4HCWo0xofE76L9O3AbrfDOCsopzKAZkiBpBVJWbQFVytIkX0IvLrfsd53rOaG0G+5ONug1Rzy+4wHdrM8AxInyzmrmcL7AWUHZo0lRU3nIyHJDmxcja1qhpChi3T9HmsUdaVp6sQ8HSQntJaKQZUTXdvx3d/5jLPTFc+enrPfXZO5po8npGilCi9L9/mMCLeFICn9+VwayD5/9lRQbkPRUNkSBkfKTlCYEHFVg7U1Fo1OoGKkcY55dcam7ei85we1f+8Oyq/8yq88+P5//B//R549e8a3vvUt/sSf+BPc39/z3//3/z3/0//0P/Ff/Bf/BQD/w//wP/DNb36T/+V/+V/4z/6z/+wH/kznHMvlUiAyY8ogSUqmso6zswtW9YxqfkLWmkEpYtEtMa6aqgGMNpPOR86ZxjmsMVO3UYHFKVLPVr4qw6nVuCenmKaiqWo0iioX5dCx2+dIysuZrpdNpNKKOZrLbLhUlnNlWNjSefjpJW61ZPX1r1Cfn3Hy9AmzByqbhdxYUJ+cIWsNyaC1xVlHrGq0EVGwWBq+oR3aOpyrpXVOygy9p217tm3LrhUnIhRpb/kgpn/H1IH3QYTvsoisxRSAjLGiWNvUDZVzQlbzUu7YdgNDyMSsUXlU9h3LXzn0ikhyXROL9MhJiSnR9wN939L1W6pmi0kd+11iGDTbFqxNhNhS20hjYTabcZ6MVGMMHcoV2JgarecYe0LwQlreXG/QKnAan6FMaR0+dMTdmtoZaqvZbLcT0TmTMUhTxX5IVA5qp2m7no7AJ59f4QfP+aZnVlsuzirqZkG90HgfGIaIDz2ZRPQ7qqai9wO7zTFKBYvlAmaKk9MTZrOGrutYr3d89ukV+y7SdhFrZ1hTTaiWpMmK+u/0/UPysy7RjUkJFZDxSFLiqLVBK8Vs1pBToiWTixCgc4rVqqa/2eP9jq5dY7Vi3miqesbTaj6tkdevX7Pdbgntlj5ldl1HnZ8AH4gTX0TDvB/Ltst954ROEt1JObOVygUUWbsyLwXRM2RMzujk0QwoI6WXORYF2OAlIvU9umkEHbIOYwwuiiKmdIl204FqjUY7eX5VEv2QsQ1EbSr5W+dYnZwwXy2oimDa+eVl4Z05XGna1rU9m82O/b6TtWMMKSaGMIAB3GG8RYsiSOonZ7pQlD61oWpm+GElzfK09LVyVUPVzAheC5+mlH7LPMhSAp2L2lBORahQl/t3Mk62wlU1T54+p66kPUjftdzdvOTq1Qs+++xjPv/sM+5ub9jvd8SUaGYzXFWzWp3wzjvvopH0x3JRqu6UErExSn+qsp9MaNjRXhZjYrNes5jPOb18xuVbb/GVb/wBnJNmh5jMbr/n29/7F7x8fcu//fZHXF3f8PrqtaQTy95Ahjh4ghakLwRp1dH1mroyVO4V9/d7wDFrGmbzmfjDYYaz4sw5xEEZhf2kXDcIMVordOWEjKsg6UhMRR3YKFxl8f3A4D3DvmOT4fbWYXPLxWyDMqfAe9PdV80SYzRNrWksEHuiClTO0GrN4EcUVKFNhXY1PnSl0eeANYr5TFJrZiJ054kkDjD0A3f3t5A056en7PZb+r6jWjiUFon+nEuF39h3qxNtnMparK45Pz0DlYipI6aBELcl8Exsdh29D8yaGqM1xCRIa0y4pqJparoQfjQclDft/l6i/ouLCwC+9a1v4b3nF3/xF6fX/PRP/zQffPABv/Zrv/Z7clCePXvGN7/5Ta6urvjkk0+map6T1Yp333qLP/Nf/l85Xy54ulyiLCCNI0FBQzWVLappgMWcMUU0TDYK6QYqB2ZSmohlcI5gDXkhpFijRLDdoSeI7M2mWTHJwYaGKmcWAeZZM0fJBmg0ejlHOYc5WaKrEU57I8UzHjSltj5lkRROShO0IWuDCRE3BFQ/YGrLcrWkmS/Qri5KrZ7NruV+I2W5t+u9VDNog3MVxgQh/JXGgTEGOdQKXGu0VCIkRZE+htl8zmw+p6qaknbr8T6w37dENCkLGTZnae4ncvRS/i0S3APD4HFYtHlwZpXKH2nUliLsd5m+h2GYkZOjaWaE4Hnx8jOJ1LRmbGI4n1VIo1wPSrPZ99htz3wTGHyF9yt2+0RKPXa2p6kiSxcJaqCjIwbNPiiSUuiqQZcO0pvNlhgTPiR6Y+msJ6aeGANdcKAdQc9oc+blZo/Z9Zj7jUSUwHxusVbhfUtWFVVfEbwDSldfpXjnnefUNkjlCEypr6pxNPMV2tTSJC4V/RUMVVVPTvVYpXVQPBWHZRj+/+z9yY8tWZ7fB37OZNMdfHxDjBmZWVVZLBVTEkU1UGADTQgSuCf/AQJckoQkaiMutRJ30oYStNJOEMAFQYBcElALFNRNqilRYEssVuUUGcOb/Lnf0YYz9eJ37Lr7i8iszGBVs5GdVvXy+fNwv9eu2bFzfuf7+w4TOYnqwllN4wyjSqjkMVZ4KBcXaxQw9BVjf2C3nVh2HdeXlywXK/r+yBc//CEpiDoihoExDDTNAmM7zs6WLLqWYy+qkcoqVp2gnSEmdv3Eq5s7Xt31XHfXVCX8LscJv7uTxTYFohKXTLu6RlctznUS5qiUeEkQ0GlEM5H6Ea8iUzZgFbXLOOtYtBVt20r8AkDmRIwXoytxyMw54f1E5UqKd3HNnIuYdbekbVuWy5WM9bbBFw5c9GNpC4sKarVa8fTZU5Q2Qg6PkeNxkPswfbU7f/n0Y/TqI6bjhuBHDvtbIef6EZUTaTjiUyg7X+FoWWUwrsK2LSF6wjQCqfjAlNZOMYJc1i2Nq4q6R6NsTbc6p2kXpJQ5Hve8+OIt+92GLz77CXe3b3nzRtCTvu+ZplFQ1LFHG8N+e8vY7xn6PcNw4HDY8dHH32K1XKFw5LLYK4qVOqpwz+4/c1VXvPed71C3NavzczyRT7/4jO/8xr/B6uopNzdvudls+Uf/8//G69c3vHjxmnGaGKZZoq1K5lXGWkNWEBVCfI4Qh8wwBib/BufueP16y6JreP78mrauWSwa6rpmsahpGylYX336Y/ph5PnVOW1VYZ1js92x2WxxxuGMxtQQk6IPwqs7DhMXF2ue/dYHgirHxHG348s3npY9uYF8lk+fXVRmmX/xgy/J4YjWwos6DoHdvufFmz27/cSxn2j7nkxi56VAGcdA1zieP6nY9AHyRD9K0TB5j7WGj799wWKx5MMPn7FeLXCrFbWpwE3c3EkhVTt3WuemaeJ43LPfb+mPe5wT4rlYIShyriA7NA0qR0iBL798yc3dHR++/yGrRcf15TlVVWGcIRY/qznP7Zc9/kQLlJQS/9F/9B/x5/7cn+N3f/d3AXjx4gVVVXF+fv7oZ589e8aLFy++9nXG4q8xHw+1+QBd1/H+++9zdnZGVVXi36AVq/Wa6ydP+c0/9adYrxecLTuig2gha5EPL5LDFMKYHPMXsrhpNVfmWapDNUPlGrIhuYpkLaFSZK1KBsjjAmXmbdwrjLI8QDphUqbxiTor6qSwzoo5Vu1E5+isEJX0V/t4uZCTZoOqnKU/n5FFVHbGGRtBh4guygxtLGAISXq//ejpx5GhBIbJx79P4AROcsWUc/Fsf8jkLiRGbWRRcyLr01pLIVHIsT4EUiFgnYqr2Q+vfLbZN1TCBS1au0dkutIREnVChhAUMRpiqgCH0YaUJ47Ho+xqlMVZ4d7o0lfNBQodomecIqMXpUbKitH3UmxME06DdRmURxPwASHZKlGWxCihicPgT+FcqnCC5mygpGTHjbEEAuPoIU/AEaslZblqWiHyTh6bIvU0EeJ9q1IBi0VLVxd7+ZxK7gXSrqhrqqpjfwikSfrHJ9+J4vL4sK3zkNMzjx9pCQlvQ5QFCavAGUXbVNKfToHkB3IS9Kyqauq6palq2qZhGqRoTSkxRY9zFZkiYbWOmMAEjdWJphLL75gyk4/0w8jh2HPxwOc+p0TyB3KUePuUNTFrTLcs4XAVWhkM0r6wyqPxkleSpEjI0ZWCUiSoTeVEDlpVQiBOMydDSN5aSfGdYiTkdFKbiBJIF/K3pW0aurZltVxQtQ2uqmEcTiTwTMZah9YSL9F1HWdnnqZtTqjKw3b0w6NZrFnVNYN1+OlYTOJGVF+IwkpJKy75E4lY7jEoK+2uXKRbcq/V6cFRZJyxGCWVv1IabexJihvjiJ9Gdttbtps7Nne37Hdbhr4/uSHPm7Q5nygEGIae3XbLbrthu90yjROhjcRsUSWhW5eE80c7jnIYY1itVtStY7FsRSl13AuHTxuOw8Td9sAXX77i5uaW7XZHyoX8rMUYLydIKpPNfe6VONKWOUPBUBZwlbfi2dTWpEWkruzJ8VcMGRVDyfXqmhWoBVMWtx1RcCkqq8lW7AripEqb0mOcZnm2RCtpQd4eJvohcLcb0XEinz2exHOKbHYD43CkqQvqMWWGQrqNWRWuB6SYOI7y/A99JGXDcUTGawzsj4FhEvdoV1muhoxtNKZuwTh8RLxYsuXQHxnGhFtLQTbPv7MP2GOiPeWey7pCtigmFElCJvuB4KXV76xY/4Ocb0rppGL7ZY8/0QLlr/7Vv8o//+f/nH/0j/7Rv9Lr/Of/+X/Of/af/Wc/87+fn5/zve99jw8++IDLy0uB/LTmz/37/x7f+63v8W/+3/6vtK14MASdCHqWvGW6KNHep4dmfp6ZHSs5OT2elsl5V54KiUhrgsonq3uBnO8Tf06FyTxhaFGxj0qKBpczNinsPAiUgqJeUYoi0NFfg6BI/o44/80OuZCUIUyQh8SCzFmE5ZRopkQOEr8eh8Bm27Pf7Xj55o67zYYhZAIiU1U6kRFjLR8SPgq5VdxApZ8cU5JUSysqJmNlgVNKMmp8CIzec+gHjsMoxNuizJ/D6YxxKKVZdLbYqxuabsWVstTtGZWrcdWDdoea8yIMOVu8P5NdiK3IWYmV/TgRgygFtDEMQyCnkWk8kMLIdBzIaKLz1M0Z1nUYKwu3PYrDYn94jQqGM2vJcUQx0h8ObLbHYhOf2G4CPmRCMnRdy7Onl2UmTASviGhWZwtcZek6zTBEXt70DP3I4dDT1ZbaGfbHFmMUtzc76qblk+929BGgYx54dWNoWjFFCilyHAa0tpydnRVZH9LSyJEYBGGaxbk/73BVkb9HIZZmA22t0clJEmxV8fRCIuDfDBt2447bV5+zu/2IYT/w/ocfYS4MbdXBFDE4fIr0g0fZCTVNJaW2Yl1VotzBc7ZsiSEyDJ7tYeQ4esZwnw0jA9yjplssmdoAxTtR+w5tErUVSNnlgCFR6YjJAUOAKO0MR5CeOBpnNF3bUDe1kDpLDtGclq0VxOjpjweBvUklbFPTdQ1VXXGxXlFXFV3bUFUluylGYphO/clj3wsCWRU+kILVckFT19y8eUuKmbc3t6SYSqHyWLrZri4x7pL11TNyCgyHO4IfGQ8bUvJCoJwGUpywuvAMjjvC1DNud2SdaBxMXtyqfc4lKVtcr3XxMEKBrWqabs00TXg/cty8xY8Du81bvA+s1md0iyXXT58xjuJaO02T+GYUZVHTtrRdx2q5ZLW+IGWYgtjRO2dJJp3UkUbFx3Ph/WRG9p5mWfHe9Zq7zZ7buzte/vQPuLt5yf/jH/9TPvv8Ba9evmQcRY4bUzwpN1NKhMKvkZC7sqirjDLQVoKQPH1ySdvUWBPRKjP6wLTZcLu7w+iMUZkPnz1hvehwWWFdRdSe68uWj3/zIz799HNWK2EYagV1J74ph8Gw2e754Q9/Qr8f+fKnr/jub36XZ8+fEifF5uaOf/qDDfUy8p3n4r8EMPUj/W5PtgvcasGTq3OU0hwOPXU/opodufCWVDxAlLmNHFBqYpgUX7w8lIJMnTZsU0jkIXP8Pz6n697y+esDbVXTtZ10DDLc3G7IGbrf+A0WxlLVHXW74PziEj9KoUqxflCmkfsWptKSFz5K1Tg++fh9nlxf0LgGZy05RELM+GFCSL8Q1f+PISh/7a/9Nf7+3//7/I//4//Ihx9+ePr+8+fPmaaJu7u7RyjKy5cvef78+de+1t/8m3+Tv/E3/sbp39vtlo8++uj0b+ckuOrs7IzLy0vx6zCGj7/9bT741ke06xVV5bBGJIda3e8mqqglNfRrCpSZ9X76/vwflSptA3VS16hSoJSOP/r02/dGbw8l0LF4rGiQvJC54CnKSVVIcAIuzIvMz2jx5PvzzEg2kMaglaNRmdaAzRqdFDFk/BiISGDgdn9kfxzpBy87jQIIpyy72xk1mWXBsxW+nN5si6zv81uc2KNnJN3S+8DkvahyUoLZ/VPdoyRGa5yrsdbSNBVV1eHqlqY5w9oKkzdAOH3m2Xdi7mRnICf9gIgsKZym7AxjSoQM05QIxcAoF9LLNIk0WZADLeoBbQhhYgBuNxk/jRz3e3a7nv2+l6TiFBn6RIwQskWR2WyrsgokxiA7cG0NIRhCyPTjwP4wMA4TfT9hyzUbJvEq2R9HQtIMYyC8E08+G8TNsmFrHdZJuyKV++Wcleh1VejT83B70LacibKn182UwMBYij9BTZLVNJWhqS2NM4SQiH6A5KmsRqtESkGIpnXNatFhcqBtKlyOBNXSdQ2Lti5SVuFFacAmz6LI28ULJ5LKLvHhGFeA1Qmdc+FN5CKmDxgVsCpgtcGmcHJPtWSsKs9MKhynlMnJnD6/KeiFOCMXc8aUSCGeAhmZza+UI2vxKpl/TyGZQ1obJjuhrcGU14Z8Qkf0g2tvirx0sehYrZbUTcPkPT4GNBEe2HdpI+1V5QwkMVlMYRLfleiJcSJOPSmMWIRv4seepPU996BsAFCZKfl707eU8cGj/USYRoKfiMGDKUWunkNAO1yVKM02chYiayyFR8oJpUwpUDrquqbrWhbLBVXTgjakPLddOM2NWYlahHw/PufnOvqRFCpZfHNE5SSOqjHz+vUrXr9+hQ+RlCVhXM1oTs5l0SzzS5l3QwoIwQds4UOFIM7YUu1GcvSlGA3CeVKJZdNATFytO6zVDDlQN5b1uuH8bMHF+VrC/YymW7UobTj0hrppefP6Fq1gGj3Dsee4Pwrhtq0x7QpdLR5x6nSJeahMBVrj6hajNSkbjGvQVSsoREiEwcg1omLykboO5bNJsZlRMocnynWSlpezjhxFueaniLMWow1NLS1kU9KIU0oSzaHvIxxCKKGBBRHJBZmnqDqNUiyXS5xzqHSPCpbVQsajRta3d1qZv8jxx16g5Jz563/9r/N3/+7f5X/4H/4Hvv3tbz/67//Ov/Pv4JzjH/7Df8hf+kt/CYDf//3f59NPP+X3fu/3vvY167qmLhfz6w7nHKvViu9+97v82T/7Z9ntdlR1zb/3F/4Dnjx9im5rklKELOocqSBTcfDT9w9K5r5QOV3PU7Pw8ZvON+rBP7+ujFCPfuYeRiNL/1GjBAqcF/7ygOE0IOTA06m987nvHUAfxLoX/4HWdjSN4lmVuHKKWjWoaDkcoyABYc/b27fc3W3YbDbSRguKkEQKG2ISqagPUqgg5zY7xwoEKG0OlBIZc1uJ1bmRPJjDcaQfBra7Pf0wysRitTjeugpnHc61WGNpmlakmdeXnF9cc3Z2Sd2sAcVP/vCfMA4iu00pMpaAOvFlkWJoKDJz7z0qZxZdi3E1ru7YbHeM8chxyoxDwvdywyyw3028eXXHctHRtpXIR41mGPdsdj3/58vX7HdHmXhysQrLYsB+fubQBoYQeXuX+P0/+ElpW2UWy5aqtqdEzzGMhQwqScHWGFZnlmrRMQbJE9r2I1M27IdANumh4ztiOiaIWuUqLq/PcZUUKP0ovfhMoqojx35T0kzDiTs1j5d5POpyXiEGYvBin61BW02uRAl2ddbRtQ1nnWW/69m8/gKdIt/+4AlnCwvpwGplWK8X/OZvfkC/X/Pe5RnaGrIxdF1H17XEsghcXl1itaHfDSdjnylEDsMokfWtPXFmoNif16Iy0DnhTCKrTOcCzno6PUhulYkYDZWGyigqYyA6ctL0hwOJjLPI57K2cMmKWsZ7NpsNfprwhVuRU6Kua9quoWsq2qZmuVjQNDXOSqDaze2tKIGalusn19RtS+2EH2RdUe5Yd1L+zbH0T589pWk73ry9o75tSK/fkMII8T7O2BmFsQqthTDXNPK6mqdCYE6BMOyl3eYPhOnIfvOmzF1S6GltcLVFKYM67pn8BMkzhcDd3VuqYaANQYp4bVmdX9G0HRfXz9HGiOdNKdh1QU1TaQf6KEGoCo02BlvECRIMWorcpiFiGYKYuVkDKklGkFFzhsz9jJZiYL+9xZiJzV3LNCWshqnf4/dHfvSDH/LZl6+ZkgJjwVTCL6Goz2LAVIXroqUNeuzHeVqkPx6pnGMYt6LWqQsJtrSrMxFTDDqzh916wSff+TMslg13+7c0Xc16ZXn27AyN4ur6CcvVkuXFAm0s+z28eX2LNS1vXr/mxRdf8IPf/yE//sMf8MHHH3F+uWS9/j7oEv5ajnbV0C3PCF5a9HVtMdbSrtdUVcNisaTfH+n3R477HWGapEWvpJUv/68hR+GE5HkYKFLOohjN0hrWZdO2WIg78Jx9FRFEaxj29+1OrUvhIqnzVeG+jdME2qKNllZpAQdi8Ce58sxxm4scrTVhHPHhl2/z/LEXKH/1r/5V/rv/7r/j7/29v8dqtTrxSs7OzmjblrOzM/7KX/kr/I2/8Te4vLxkvV7z1//6X+f3fu/3vhFBFub+seGDDz7g+9//Pv0wYK3l7Pycum1Ba0nZpVRyCeYWTtZa/nlCUGaUBCiFxKk4eVB5qOK/cF+/PKhQHtQ4PwthN4CwK2Z7bdlmSF1UUJm513vq87x7ZOaktxngmSkiztUsGktdJaxNHGIi9xPH2y2HmLibPPvdnsOxF5nYFJlCZArptMsIRflzem3uuQ3z500pkJJ9xG2YvIfsGY8jwzAIfBxkIrFIr98ViWbXLamqmvXqjMViycXFJYvFmqoWkm3OjxGAEAIHfyjpq16uVVaEaSr9f49WmWwL0qVl15aJzEm+M/AlxUPG+5F+kNCtGERiu9luOR6PvHpxxzR5Qki0dS3S6Zl4qIuRXorEmAkx4KdQQgkD1pXeODCFCaUVVWPoOsNyVWMMQmj0cu7GGozTpDznQD2+1cX9nBAjt3dbKlcxjh4fgnCJjolpkhlZGyPN91MnnlNo46zymaWZc4sj54SPgWPfMxwPkCPHpsZasWx/7/3nhGnEjz3KKPpxkN2ognbREsPIZr8TblCCxXJguVqc+C1d77FWYHWSyCBDzMTi49IWdOPRs03hVJXn0miojaKyUOuENrN6IZ8cSrVCWq+q+PhkMTKbxolhkNC5wVqmccQHz+GwF+fgaUQrJfJiJ20da6WY0VombSGCG7DgXEVVN1hXYawtu0d12rzMjrKK+yySuq5YLDoury5Ba7b7PX5MhP7B3KDlGTnxSJVcBY0QxAWl8yTfM+w3TMOBoT/gp7HYjcu4s67GWkenNM5PHPd3hJg49D1jSEwxUzUr2sUZq7NzkVbXjTiyzsGDWgvpWutTOrs41GYyuoQj2vKzpsQoAMqckD2yONpqYO6Dv5OZB0qhjWXykS9f3tL3gb4PZLXBB9jtejFhzCXQMXpp61VyL3IWNOd+A5XQJQAyFfRI4jMmog+EYO5DC1VG6TnsVQoWbRSm1tSLiqerKzFlixMKj7WBuoGmE84dCqxTOKdpWsty1XBxdYYuGxkhFA9Er1A2U9X5tKZkLXOTyhqVFSEI/6NSnqwN2QdycSI22oKTQldQsrJJzkhA4thTV7WgGVoXDGN5f32VtLylHW9PaKBGKAdlz1JO7T6Di5wZj0dBpcZB7nnlyFGLSrXENxhz75E1t/DvK6ZvdvyxFyj/9X/9XwPw5//8n3/0/f/2v/1v+ct/+S8D8F/8F/8FWmv+0l/6S4+M2r7pMRcof/pP/2l++7d/u8g/IS8WUpwohQFqFCoi83W5t8EUJPiXPDSg0n0R8ghg+SNeby5DTDaFOqIQCz8hd6kZolb6ZOo3LyqPX+jBzS8FRBbkkna15HxVsWgiFZ6bzUv6fuDNfsfeT9wd+xO07UMgxchQFruxqHt8iJI3g/Q3c5aFT88um4iaJPhQFjoJntsfj6QQ2W/2TJMXT5Ao1vZKgS0TYdN0XF89oeuWPHnynG6xYH12AUqQEetE3jrvQgGmaeJw+4YUIjmmkyzZe3/i4mSrMFZLsrDOZDwpj2RK6m7pS1e1QenIOB3xYeB40OQYCT7w5Zcv2G2PfPbpK6rKsj5rWSw7Li7W5X5l+uNBTPeKp04m0Y8Du90RtS0ZG0kQOp8jbed49v6Kpu14+nyJ94FhPNIfPSllmraibh0xexTv9GxP0G1mmCbevH2Fc5bVsiu3X9EP4AOgFhI2GBQ5B0jhBP1rrU/oYIqJYRhJWXgBIUWGceDt3S2bu1vevNFUzjL6gYvzNX/63/pdDtstL774DIzi9rBj8BMxZ1bnC3zo+dG//Ewk6/uB9fma9eU5q8WCpm5QtDjrmPyIBmqtGadIDAKP11YWx8djXAq2WJ5ZqzSLWtM2mspGURgUdZ0qk6wu7s1CphaewpADOUsrT9ZMMWqLITAMg4xfP0kB0Z7TdTWXF2el6E4CZxtDVVdF3VZRVeLA2nQd1lVlQyFJ5/fHPTybM3Rti6sqPvr4QxarJW/e3tKrROg3p9+wRmOre3k4BXEgJ1RMxDgQ+w3j4Y6bl59zPGzZbt5ASrSVQ2uLMo66W1I3LdVCQiOP/YFpGOj7PUodMOYObWsZj++9R9M4ukUnhZdrStExoz+cyOk+C/oR5g3Rg/lJcscoYaAQS2EtRcB9gOO7lH+lNK5uOAwjP/79n7LdDmw2A2Mhtd7c7BjGWOr2SPJHmsqwrB3WCIkbPVOGNTEl+skSUmSKEWIkhUR/GORZMAajDW0tGwXroGAJaKuoGovtNO15zfsffsRhf+Dzn3xKSgeMPVK1gWaRiUykKI1HbSPNQnFpVyxWtTg7J8XrNy/Z3t0xjiOuXnO1+vD02aMKJD1iVYuOiv4oRG+TPN4Hep8Zhwk/eblGTowRH6bOC/rRc/P2FdfX17hG1JRKGbpmKQXrIxfwOV9NJhVtbBnfpT1TCtE5DT3lxOburcjXg1jiGzohMGexkMiJ072fx0FVV8QYxDX9G7R34E+oxfNHHU3T8Lf/9t/mb//tv/3H+t7WSq/YpkQCwtz/pRQUc5/kYe/zq9/iMVQy/+A7PzGjFurxg/bOL91/Zy4uyuuUeY8Tl+Wr78zjyufr3fjmHdZcSUuWi8LnzCEmXhx7bBx5c7NhiJ6tTsSc8KcQOeiHsej8Q/EM8ExTKFk8pR3FvdU4SXqsRiuoNJMPDIMHbcSwrDC6h8NRuAsJ2W0phbEO62oWi5UgJpdXLBdrnj17D+tqqrrFh3nQy0P0cEylnCTQbByZxlEmY6Vpuw6lFN4PQCLmSG2gXThCrDFmot8Zog8MMeMqxdV1w2JVsVxm4ZXExGZ7ZDiO7LYHgvc8f7ZisWy5uj5nuWrpFi3RizfCNPVMPnE8Tmitubo8p7IWpxXjGISsWhQEMkdGNndDgWQVdaNxlexQlZacl+itkLJTntvn5Jx59fI1lgHbrRlGzxdfvjq1ZpZLuZaZCpSjWTZI1LslxISPj5r9p0JFK1i0Dd5P9P2B/nDk7dsbxmKz76qGqq6puwWL9RnP3n+fbdvQH3asri9ZXZ5zefWc5eqSmMSzwUeFshVnVy1VW5OtYkRUBZ+/fiMmedbQNRVnF+e4asAYy6HvGeIRd3aBqR63c2du0RyhYNRsgZ/KuCwoh5LPF2Nm8iMhiMsvOVEvFyenY/HREWXgTPrMpf1QOSEer9drlsulFDExSjFS1yyXK7kmdYcpvBpnxe9HEI+HaGu+f2bLM66UqAHP1isAri4v2BA53L06zQ1zSOMJlM1ie+7HnuP+lu3bVxw2L+n3d2w3b5mmEWsqXOO4vjgvOUKGplsWE7cRPw2YL78k4dkdD2gFlTMcDgf22w2bm5cYlTls77DOsVieUTlBe5xz1LUopsRLJ5/mm/mj6rLAGS2ohJ5RyoJqufL9ub2jvmbG1VoWOWUqpjiyPXqOh4GxGBoqYNk6rFa0Fiqraa0+WanHrEgJphDISWwiKmtZGi3k6RxpnbSbuoW0o9vlQkjKStDPMAUu1hVdo+kPrznsJ1Jc4Mcdt2/fopRhsViyXC5YLBf0Q2byicP+yG67Y7vZlfyyzNnVGevVmqcfnBPCxJs3b/Chwj/YcKU0EeKBpjYSNZA1OWQpIhk4qkGEEDmV9qTG2ebBpk3WiOWihXxF10rsQNM0WGNxti3FeDgVDvMqkk5zq6BO1rlToRlCIMQoLZ0oNIoYFYd+Sz8m7nY3tK209a1dYEyFUg1aG7pFi7GGqq4Z+57o/f1z8UsevxJZPPMxk9PyTOhEdpZzNMV8PHo08qP64KvHz6i3HtJTTt/4Je6BTDz3D/mcOPL4xR62m37Ga5yQlfuKSWnZ5fQx8XIayf7Il7c7xjAxqISxuvg7CIx8HCamaWKahDA4jDIhxCia/Jxmgps6tcgm74s7qcN7+R2Kr8V+u8d7jx8mqcaVEf6KUhgj5lXdYslydSYmeqszrq+fgTLEpEh5JISp8E0ftzok8TQwjAN9fxASrXWsm3Oss3AMRWUT0Baa1pJThdU1byvDOEiCqdaK84uGbmHpFkhw2BQ4HLfsNj373QFrFB+8t+LsfMWz957gKodxljBG/BS5vbslkxkGT9c2XF2c4bRC58huNzAMHp0k9dhk+Sz77SAL6BS5etKyPquLFbzCD9Jiyqk4pz4YW29e35CmLdfvOfop8PLlGw77HdvNLU+ePuPp02e4aoFzDXV3Xq6zASTUTYaK9LlVVlIUKUvX1Iwqs996+uORu9s7UhQPD1vVsgPvFrSrFdfPnmGN5u7mDddPnnHx7BnnF89YLJbErJhCxkeNdY7V+ZJsMslkJkR2fHt7RGfFxXqB1itJN65Eitv3O7bDwGUIvMs2m80AnZIxKwhJ2Zrn0gQ6PSbSchuGAT+NjENfxt1KcqKsZRgGxnE4paBLsjCY2sm5r1csl0u6TtAp731xnq1YLBY0TUu3XJfnTj1ATx9uKL5mQigFvtawWi1QSnF5cUacjqenHUo7yWj5jOQC8U/EcU+/fcvd68/Yb17S72/p+4GYM4vFBV234PLq6YlsXHVLqmZRrsMR4xoSR46DF8VSzvR9z2G/ZXt7I06zRgzczi6uaNsFmktoOyFGW12Kjvum1dzl1lphyx9TCgalhLBsFOJVo2YEJZ9mvPtrkwtJWwzJpqjY94HtfqAvBbMxmmXjqKxmWWmsBqsz81bLB4iImSBZiVeJszS1o9aBSkfWHTSV4vKioWoc3cUKyMTk6fcj/WHibOVoa8XQv+W4n8jxOX46sL27Y7k652y1pusWdF1HCBPeTxwPR/a7A/vtHhC0rVt2PHn+hLNzi9KJT3/csN0lPnuhTvvdFD0x9ZhFK7lWSRPGxG5/FNfhIG2kmZOktZB9tZJAP4pMXLeN+JkYKUK6Rlo9zokXVQhTETKoUzZc1kVhU9CPuqnQSqO1EVuPaWLycm2tc6AyUxgZhp7DYctyVbOMDYvFE2q1IqlKsrbaFuscrq5JMdIfvllxAr9iBQrwSL7mZk3N3PLTYt7zKOtDnTaqv9TxsJw4feOXeoGyxXinAJpl5jMqok5n+1UE5WR8pGThjiUoLafEoR8YxkwajyQ/svMV3itGf8SYQO0CdetwlWHwkWEMDL2gJuMUmaZAf5yYRpHtzsxvP0zkGDkeDxir8YuGGBTgCNFjtORAxBgFjtQapR3Oik331fUzLi+v+PDjb7NanrFcXOCqhn6Q9k9GnVCbumoA98gvwlhNt3BkNDEnuk5RVRpbTWgdca3DUaH1Eq1gd9tz2B8ZhiMxiOGehMdF+qEnZlEZDcPEOAZIkboE4KHEX2DsRzY3G1arBcvlgk+eP2HRNnzyZMU0TmWRUHg0f/iDn/Lq1RushUUrOUMpZYYpExPEqGCKHG8H1gtHah1uIYvu3c3IOGWWd3vqxZK2uh9bq3aBrsBpy5gCYYxYXXF99Yy2XROxkg+XIsf9FusGCT0EuqYqfhVBcl8UhRCIyE21prWW1loa6yTdOynJD1l0jKNnu93zxecvqK3me9/7HslU4kViatp2ye/8zr/J82fvs1pcs9/d8fbmS7JN4DJ1JxbdOYIfPV+8+CkpXvPbv/GR8HhyEHfKML4DBWdi9JIxk2L5o9lsNuz3e2xVl51aKwtRVdEfD/T9geEgCFhVWRaLlm99/JGQ9YLH+4nN3W1BUGSnKG6eF0VVYU88q/V6jTGG6+trumJA6Fz11XbrL/LIwynQ1SoJW3xyec50fJy7ZLVIonOcSDEw7m8JU8902DAe3jIc7yRHKGve+/A7dN2Sq+unUkQ5Jy0hXRCUuuXm9Qvi2LNcLInBs98Jv8gZOD874/333uf66opuseDlqxcM48Cnn/4Qax2LxZrVcs36/IIPP/wWF1dXVO0Sqw3FJhY1oyP6JGosJFtwZq7ZBE0MfD1JVgG1zQzDxOZuJ54qhz2jF3WhKiSsaRhQzuCNIwRJZRL5dC4ISmYMEuYZhp7GOYgNdaOoa8VFa1i2hqcXhrbVXD6JWJepnCJMLWHq0KZBa0vVZlwaePvZj+n7ibMOtJkYxyPTNDCOI7vtls3mwA/+4A84Hibaalla4JYvv/iCFy8/45NPnrNctPIclhiy+aPXdUVtF2AUnsikM8kmbOuotMO57sTva0v0gjVO2utB3JKVTiiT0Vk8nmLM7HcyHyrGEwIphW8sXCEt+VFGU3Wi1fIhkNJEDJEYglAAYioJ4pamdXz87d/Ce8+xPzI7E1eVICgpJcZp5Pb2Bq0t1lVFJfaOfcAvcfxKFSjvtpdMvkdaZ8Zz1veck5k4q3/etVNf+eJr//noe/mdbzz82Qd9w0fSHx60mtSDE/x54NiJSMtpBzIzWn2ITDky9UH8JqK4+o0eTBRzIGU1ymhCEBfUyUdpCRSirC9mYzEEnBZmf/C++FeMZYAb2a17kZ0qJWhEyhFXV7KbU7bk/7R0izWr9QVnZ1csl2usbjHGluRNitxNPre07MyjxcAYRdtVxGTwUdG0mrrWGCvv7bT8jrXycAzDyHAc6fupmJulcv3FMj8myaoZBnGv1VmLHH1G5lMmToHhONBWNXSZs0XH9cWa80bIozEGjmPg9bbns9oRYikCrHAhRKatiVGQrRkFij6SojhfalskmUFiBGx8zEFxxmCUnW+1TBpGJL62eMCkItv100COEVUlsWOvJAeGrEvdO7vRyIJilcJpTWUl6FGRCTGKwZpzpJgYR89ms+PqfMXlk2sOU2IICa3lWj95+oy6rjnsJl4beP3yp2idMSiqylK3Ff0xME2e/XHPMHSFTJkLhC08noetVCF8x5OEdN58jOOAUgoXY8kPEYdLZ7Ts7vZ7+sOeFAOVW2OMZtF1pJwYxwE/TQU9KZNxiuRcru2DsZZzPrV22lYSwp1zGGvKInP/s39UvTJ7fzzcfxit6ZqG5mEQD9LqMEYkoypHoh8IvsePe8J0JHrJ6LGu4ez8ivX6nCdPnqKNIQUvJEZjaZuOqm7YWoNRiq4Tl+XlakUKEymM1HXDarlisVjQdh05RcbhyKtXr1BK0dQdq/UZ54c9q9WKbiFeGdZoXEE3TeGozPsINaMhyAbwnvdQNocZVH5ciirEbiGFwHDsT+T6EHNRDMkvxhAIKhOjkXGRJM1bXlsVnoy0sacgRnaVNuRKUAenFbVVNAZam1m4QF1B10KqHClaQizPUgzkMbF/e0NIULtMNonMJAt59Hg/MI5HNne3+Cnj7BpjJK/n5vaOfX/HaukI04qL9fKEXtzPZxrtxK8l5kgsRnPaWZytaNqmBKwmIbbOflhZJPI5KyH2qvvCJ8WMn4o6KadSMIrJWs6QDRILUYw4jSteSNNU5n1RSeYSdzIrcay1LNZLYgzU7QI/ToRpkngNZQhMxTBO+DJmCpyY/b8uUB5MAvOdyvm+jfI1Iph8+p+fc/ySrZtf5kjqMQfmIRdmxkvMz3nzkzstmaRVsVIosHDxGRA/E0WIMw/C4CfPwR84DhN1YxkGj/fiiCruqCWC20tPNvjAlCFqzdgfiUGC48KkCH7El1RYqcwVEDFWUxc/BO9BaYvSFavVJdfXH7BcXtI0HSmKOmAYghRZZOntO1kcZbK7vwbrs473f+MjDocVh/2K6AMpREKcyEwY10gP3sLm7sDdZsvL12/ZvN2fwhoHHwg58/kXW6pKcoOMyRiT6VqLSpLMnGMiTonejwzHQO0c61XLslJcLx311XsAHIeRN7c7Xt8eCD4y+iATgIJlK+2byYultbGl8ZiBrOj7xGJ1RbfuwHQopVktzzHuQe5Shv3mljzcsUJcQL/zyScc+p67zUZIymmgaRTKQZ/2kCH4QNu2XD99wmqxYLlcstvtGAdBKlSOEAI6R7qmgotzbGW5u9uwPxxEaj5NrNtzMoYXL2+kUPrYsawdi+LkGmPi+tl7rM7PibmRJOf/5z/l+QdXvP/BEy6ePqVqO+5u/iXBe+qmo2patJVE2GEaMc6xMObktwBSRAoHJJTxXrxgUipOwhGVlBAg/cSkMsf9nu3tLeNwJMVQ1PqRH/3gBwzjwKtXL0vRLQWYaWpySsV7xJJT4u7u7tT6ubq6OnHbTEk3N0XGW55C5sX3FzlEjZPF8CpMGJVKy+P+cEbRWk3WFckq1NiwHXe8+vLHhJComgXf+u7vcHn1BFs4Bcf+gNaaZddRF1m0uHgOVCazaB3f/93fLWeg+Pzzn/JP/vH/fMoP+uCDD3ny9AkxBl69esUXX7xkGHre3tzR3N7y5s1rtIbjYcu/8af/DE11TtVKoWbm6RZxVU05S47R6bGVz2eNwmjDOIz0/SiIZjlCSLx9feDt5sjt257dduB4HEDdS7VzzvRTYIoRnyRjaHYsndPUtdYsOuS1D0dSymz7I7UTbsaLt4Hbjef1myO1haulprLQVkpsE5Ji13sJS3UKV1uuni1ZnXe89/EV9SJQLye66jUmHzlfGTSRs7PMMEh7sa5q2nrBmxvFbjPxv/6//iXWGN57eoFxS5L7FvOCFPJIinsxX8PSNO9jXI1CY5XB6hofB3zfE44DKudSlAvJ2xhD03SlMJEboTJoW9SWRfGjlOSCJyUF3BQCQ4horYkzTSCBUQZlawJC/re1xRiFtWBMgvGIydAp8FbjKRsEcll/5l1yRs3KyXfcq3+Z41eqQHl8qEccj3n1L52e8u1f4KL9jK3Ro998CHh8zfe/7vdm578TVxYewduPfvVnkGQeAjCnNtaD93nY6b03dZOeow8JRl+8IAIhSjESi459JlDNX0ciWcWTYienKEz9kIVvMnqSLQZpTk4uFyJdyrlYfjdUVUfTdGjtgBL8hrRBZmK6QYlM7uTzcn9djNG0rSMn2e14NSdGBzIZY8SETfgrEzEJTB6TEM1SkTzGlAk+YrUgDc4qKqepKyGwzvVeSrlc/nhKChWbcUGYUoZhlBbR8dgTY5CJMss4c068AKoqFnRMn+BqrRU5KaIIbU7hdU3bgHKPxtg09sTjnqo/gq2o64qYE81QE1ImlIC4HDXaWTGdOy26seS2JIa+Z7/fiXxUKSo4+egYo6nrmqZtiDlJiqyVIDmtHbP83RTUDO3Kri3jakumpl20VHUtMldjqa2j0gaHmFFVrkY1HXXdiHQzZcZxko2FfoyWnUz5KKZpelYh5QdohDxBOatTgNo0jZIXEyPTOGC04u7uVhJavRdUZLGkqiusMYzThEJQKqWEc+K9kGdDCCe30vtxOP99evq+Mk3krzz8DzlF0oal3BP1zuQthFN5BhK6kE7l9ZyrqLuGRbekbTsx7ppRpsJ9kN1yIidR5+Uy1mZDrbpdMAxHqqoqm4MB66yo6NZnDONIUzcEH+hjX4jEI4f9ns3mjv64p21qumUn46gUaDHff0oz79rLDi+TSzaW4ugDfS9t4PlIKXPsA8c+MI4B7wUZmVUh87qXMhBh8gmTVPH4mKXCwm8RlEHGf0S4dDGDTzD4TIqZ6COjySgPlVXUTuFDxgfY9hNjiNjK4GpDNhqfoDurWeQajNjIawKWhsZE1guN0xkxMU+kOBWyLPRHCYdtrKVuNe3l/dCZY0DACp9EW1Q2BY1SJdMDyJokagPJXsuZEJJ8Pwm3LJdnhpwxWQjkJzfpE7wFOQr6QkGbQvDiLpzvxx8FjVKlDWydKXOGvl+nyiJz75gursgp3Rvo/SJL7M87fqUKlK9Uae+2XLK0fYx657//rONPCDmZT8mXvx8m2zw8tT+KG6PUfVEkjrUPixTJqdAlx0cm+/InQoiG412PL9kqOYtWH8BW9tR7JEH0kTh5yJnkRZaa52YqijAFejXQNA2qMlRWjJuO/UTOihAl+fbJk/c5P39C110QoiGNCa1sOdsiU7TFohrF7nAsHiEP81nENXMaj/jhCEkQBaelBxqnkclH9geJWCdMdAuF0jWH48g0QRxlwq+tYb0wPL02rJaOtnWSxzNljFPibZKK3XkhFWuV2ex3KBX57OWGYz/RH0a2+wOfv3zDoT/y9OkKFQ1kzaIRfsnirC2tp/rkuZGVmN+9/OkbjLNUrWa1WnB5vmYKHbvj/b3db27o337OwUdss6A5fypKmG99yN3dHXe3G3IYCTny3gfvsVqd8fy99+iPez7/9Eds7wa2dzf8v/+P/5MXX77g+uqK5aLjkw8/AOA49IRSjK0uzlldXZ4m+rpe09Q119dnnF9cSAGlHegKpTMxB1TyxOyxFbSLiourC2qr8McNu1ce6yqerc+4WqwZpjXnZ0uMaTgOnhcvX2MWC0zbPXqGc4HZlVY4Z6nrirZtZPyVgmE228pkQo4M/ZH9fkcYe0n2jZ69s/SHHWdnZ3zro494//33+fDDD+WZiIkvv/ySfujph6HwVMKpQOn7HuecLOLWEoKXHWkxzfulJ4nZ4yZ5SJ7sB3KcHv2INZLzYrUmmswuBayzPHn2EcvVmvPLazZvb3j95U/pj3sg8/TZezRNQ9fWTNPIm5e3kq9jDN6PhOipK0u36Hj67DnH4471+QW744F/8Qd/wPf/7X+bqqr41iefcHZ+xuvXr3j79i2ff/E5zkki7X6/J37+BZeXP+R42PPbv/09KepQ4pESxaAro3AzgqJmb20pNAyw22754osXDIX8CuKH8/J24nY7sTl6ei9KTLGsL0U9lPTrzDRElE4YHUuLSWFGj9ZKBABa0VSNyFyVJ2vFmBNOaZKCKUZ0zPRB4gaU0ez6yKH37IeAj3C2aKic5k0/Yj878r///mc8va559qTlO59cc3214vL8jDNl+N2Pao6D4u3G8Opmy2effck4BJzr2KWJ4D2v3uzolpYPLvJpnvZe4b3hfH1FXbWk5MpG40CMhhBqUhLTP0w1T4Jiv5AjZEOKhVhtNeMwEXxAkYllI6GNFd6KdijjQB/JOWLLSfjpKMUIStpTtiaGiXEaydrhtOVi/URaycngx4HD9k7UcMOELplTrmqLknKSNltK8nSYGSz45auVX6kCZW7xyD/kT56//koJcN/X/NrNzjz5/Jz5Jz/44peuZbJ4Adxbstw3eGaURz0836+5t7N6QfgsnHgoat7+Z05W9SdEJc/8e00IuchhR3IMItFV4FJ1sljJqewcy99zKN4cGiU8AWkfZQxKWYwRa/OqquXhsA2Xl0958uQ5i8Uaa2uUEqXB3JPO5QQ1oi7IsWj1y+IzHyFEDruR3W5gu+mpXYPRFmtFkSSOjOkUO08syEKOJzLdPESsVTircE5RV4auMShtaRp4/myJnxJtJTJmpQ22dviUeHWzY7PreXmzwYeINeDzRCKgjaKunYTMJtk5CqVmNugwaCe8j/lTTYNHTQFFRagSKcgu5/4+C3JkrHAsVARvdlRNw0IBKVJZjXEVzkk0+mK1Evg2JoZ+KEiAFEaKXDgDDyE4hTKy46qbBldVp5GobXUKgBTH4I50SqW+R+hQYKyjqmsWy5amlWC9vj8Sdnu2QyXR9DbRNg4vpi1UdS1JxfGrrZLZB0cpae2E4O8LlJQxWgpiBSfkIIbZE6f04Mk0dc1queTpkyc8eXLN9fWVIIMxMgw9dV/TDIMUQyXcb7lcslwuWSwWVFVVWo6PdgHl0j1Gfd55SB9+mlNfeY6yIN/zoh6+3szroKBaKQXadoFSmv54YBiOjGNfuCz25Es09P2pRStvlU/3PkQhPiqlcNbSdR3DMLDf79lut+x2O+q65uzsnA8++JC2k7wbQcwsdd1SVzU5Z0LwX1lw1IPzVzyYX+evYsKnxH5/4ObtrWSmlSPlzHEMHCdR08WUC3/t4fw3zz3y8+KpJuiZStIyV0oVUzMJKs1JlIijFy8gpyuy0agkrtAZTqabvc8cfSIoA1ajnGiSY5Zn0kfF7W2CNFKbHf0uMF1JanA/KMZJMx4c/X5gfzhw7DPDKIq9EKIgSD49uCIUs/BEGAMmB6pKo4wh24aYMz5GlJJQUbFUU6QohZoxpaX+EJzjMRI/o9AxeOQiJRRBIicaizaGXOIgBKFR5CSmkY1uaLpW1ItVI6gpEh0SFTKX2UrGntFC1kUURyqJ6Vx+cO++yfErVaDA48kC/Rhl+nrg5GtX/p/1jz+2Y0ZI5iLlviEzlw/3AN2jNtVXjiyFSdlNZi3OtDGWXn1JV50HSczzgmnwPnM8eIbjjuhHYhyEaFo3WGupXSWFQpo3fgVKzIpUvDxImpBEHpVxKNNQVQvpg5+taNsFV9fPee+9j/jud38baxu0ccLsTgWynOHzrEjJFMREdmPpIW4MjH3g1X7L25tbbt7ccHl1zmLZcfWkxZjMMB2EU5DF/joFIXL5MeB9kgImJrTT1LWiqhXWKdrWslrULBYtWhuWXSsBZ6rCBxhG8FPgMHq+/JefMw4jYxqoas13v32BzgHTQmUM2VTSLgsR7yV1GF0h/ieSdVI1rUyPKfHm5pbgPSouqXXNtA9E/XDRUjRdC1PLl7cHptST9iNt03JxcYbThmXjWK7OaBdLPvn4I6qm5cvXr9ntdty+lej043FHjoH1esFqKTJJY2WSNpWTUak064sLlsslsXDcfMg4W3JXuo7z6+uTV46krCqBjLWibhYsVyuun17QNYpFo3nxhz/k1esbXrzpSVlz/eyaHD27956Jf8z1FW+PE8dQxuY8srP404hs1gmpNfmTdb5CYW1F23SSoWQMpESYJGVVpnRJV33+7Cnvv/8+v/293+LJkyc8efqE4AUp0UraHOKKq1DFpn+1XLE+W7NYLFiv1+LAaew3UvA8fF6lxSdR9dLfe0yINvoegcjKsDo/p6orUgxsNrf89MXnIsOPgfOzS5qmoa4qckrc3NyWArQ4gZrIOPSSJ3XsMcaSCiJzeXnJF198ydvbWz77/AvOzy/4/ve/z+XlFXXtuL295cn1NT4EQogi6ddSdKYYTmGLD2Up88bqVH7Lrgig+CVNvHj5ih/+8Md0jcGeIhcyt/uJ2/3Ath9ISPK6KjvveY172GpLxe+K+HBuzwz5vqhXhXAcQqQ3HqUMdaXERE1lxiRnrLRi7zMHH2m6hcRILIUMTCl6p1HR94GXLydefv6aZQMfP3F0jaZxFR7HNnS83gRe33i2Q+Y4JvreSxHiFcY9LkaTT6Qp0scdwXrWz5/gbE1SCwYGhsMdzmmcU5I/hGYcxeunqlwp2KRYzw/MuVQRUOTSog/TJLdCgbNO5vfzBXXrsI0mpUR/6PFjYDrc88RWZ+e4qgErDrbWdUQUQb9G1Q1NZVHKy03IHkCSoQNFvcOJnPtNjl+5AuXhkVQ+tVC478adioD5+LnTzc+5sA+c7n/ur/ys99LlqXvE352/d5Lk/DyLm1IvF0Z3SqJISOm+H51dsRsuc0g62dcrKD19kaB6UvKCwHjJf1WuWPGjERxDgghFey9W2t1iTdst6RYrLi4uWS6WnF2saNuGJ8+f0rYdZ2fXLJZn1E1Hzvoe1Zp75eWzZMRoab5QJ6vlB8c4BTZv97y92fP2ZoePmW7fM04NxiiG4Vh2TcJdqVzJXlFJ+DYhSm8+UWylkTZGU7FYt5wtFzjjqKrE5BObnfTCU1bsDwPbzYEUEllZmrbBVYohTPgcsbX4MMi1SqAjMQW5/DpjTaBpRFljnGUcAj5GXJVwleHisma5chgzkQmPKuvV2RmNGfnp2y+EwKeF17NcLgpRWFJ4g4+8/vJLlDH8+PPP2e8P9MeJmMBVNdZZlIJ1iZ5o2k6SqvuJhCJpGIcJxRFrWrQy1JXFOYOrHf3k+cnnL1gulnTtgqpuqVzDFA+klLG2pqoamqZBG4XPmj7UHLxjzNOprRVTZugHnDM8eXpB2oyYIWLtw8amICC5BI2lFAsCVuTiSvgWTV0LwqRmgzMlZD9FsX5XJ6TkzZs3zEZ1RhsgF++ZhLMG6xzdQvgdy/J37RoJUctIzME7iOlj1CQTvPChUBKIKdk/JwyzIJKC7J2QvofzQmlZKKQlO/RHdps7Pv/8U/r+yOGwo2tbusUSYzQxRF6+/FJUibNNvrNlnsvCwQqh+EQV5Ya+J5h6H3j58gXLxYLvfOe7JXx1VXbXGe8DfvKFI2VF2u3cfcYTlBbBfVFyv3By+uTHfuTt2ztev7nhzc0bnj+5wLbN6TXEbbrMfcXfRiYCRXpgLIm6nxPzw9kjPzwXSt6TEHdjypiY2RxG3BhoCqeisqaca8IHTc6yuCqVS8tP+EU5ZULU6CRF026EKQE3msqAygGfE7uQ2Azw5pDpp8gYxAlXhkhg8vH+uQYqZdDaEb3Bh8zubotzFdZUxOBRSorZHD0xZym5Z/XUKWOryKwnT86SuSOeKbr8W/ghmVRCHhM5B8ZBTOBsFArANHiSF1fY4AMwsFd7rB1lsVIKY7aEMOKMEc8VZB6NZYzlnJj8WICBdLp/3/T4lS1QyhIhsuLynRmROLVP8v0D9U2Akn+VfZRC+DD3x+mpln+WAfHzjsxMT5rbL+mEmsyqA2XdqTCbWyYpZ3FfLK2LGTonT+UnnUzeuSQOl8EtCLVMe9bVNN2SyyfPOTu/5PLyKddX16xXK87P1ywWHR9/51s0TcdicU4o/ireS06NEC7zg/ZUuWc5FRWWfMaUHssRhyHw8tWOtzdbbm+2HIeRpnXs9g3WaumtGkXT6ELMFWMjtEiog4+kHEiRExFPaUXdVazOOi5WK2pXsV6qQtrbMXmJq9/uAy9e71kuGura0i4qrEscQ09OCdsoUWapTCKCjqgYyTGjVcBYRdN4MQxzjqlEC7hGLKwvrxu6zuFsCfp7sG6tz89JTSb+yxeMfsIY0MaxXq047Hfsx55p3KPo+fKnPyXlzB/86AeEADpXWKeo6kZsva1mfXZO17a0iwbGCR+3pDLDD/1ImAJdJ0nEbVVT15aqrujHkR9++jnf/vgTzs6vqesFzlYcdwIlVw8KlJQ1PmuOsWYXKkYlWTdzgdIPA66yPH/vimCP6IOE1z0a4TmRs4aUiA9yhQr9CaO1FEMywE8xDOLJoYrFtyLEwOF45OXLlyJjz4pl10kC9OTJMeDqlrZpuLq4FDO2blFs7a34xsxV/tyPnMEDTv8EhDSeCsNw9lQ5LdOlJyo8g0gKgRwf76rnjJhMhpDoDwfubt/y6U9+REqS1bFaSAGVkrRbXnzxOVoprq+uhPw7b0xKuzNFj9LFir4803NWSgieL774kpzh3/13/y+sVqW1tVxwfn4uJo7jJAZhpagDhTX2HtXgnricVT5FScU4b6DgcOx5+eqG16/f8Pr1Ky7WEkZJmZumIHEYWmvUifhZdt9lfkMpaY9pXaTKEjJBIYnOkHmmAFPlXKQFmAlhEMO3rsM5xUJJjljwgVRQ4BgUpMghh1ObDTQpaXTW6GyIo8J62PZATvhhxMfMLkjg54ApHj4R0LIZS4GppAPPh9OGStdsR42PsOUO5xzLxZoUYzEkjKQA6FTOQwa/LbEQUpyITYIozezJ9Ve4idLSy1kcnWWdiIyDOH6bSQrR6MWBlwR+8gQvSe+iohKPLZD5o2qcFNJqTpAPeD+SUsInX0jyFQrZRLxLBP9Fj1/ZAgXuCwj94F/qKz+Rf0alob7y03+yh7r/6x5iuD+FrxRR4mWScijohyy+uYwwW4hRpq4hJyoN4zTSthXee/pxIHmZmA8bg1eibNFG0bY1ddXQtrUoIcp7kRPL9Rl1t+CDj7/Fen3Oe+9/TNetWK7OWbQLkdi1Na5yKBpiMByOXlQzxXMiCu5XPub9h1JlxzlD6LHk9zzGB/NJlWCd5EuElLh5eyBnmCbhV7hKY51YyR976WsHL8St4BNeZ4IXzkqc4PXLPcf9yKvlnrpy1LYmZ42zhspCpRNnSwdPljS1pXKaj55b2iYXiFTkxT6JImryBh8Dh8Mk7zdRWhJzbHmk6jJRKWLIaBU57DfFm8IRTP1IGq+NxjjDetWRlWbb99y8fcvv/2HkOPQch57z5Rlt3bLZbjDW8uzJs7K4OySPyBPiSC68h0SSEJKYycYSfGLyEhzptEGpPSl5FiuH0pJ6m2LkuB/58stXjMeJ3/mdNe1Fh7WOEDIxHjEmcnGxYncY2e1HKQo9XJw/pa4rnjy7YtXWZOWwTrGwivMVKDVh7f2HViBOpHNZXHhQkumiMEUp1DYNOWf8NMq4mXkrpQsZUuTt3V0JsZS20Hp5jtMSK3Dc7/F+Yjge6JuWHDPLxZp8Bk2dcK4iuZI1osA4iy0STtRDxEdCCf/gD/+AcRwxxnJ+ccG3Pv6YubTPQYqFcRgZ+p6+PzKW8370wbXCDyP9Yc9nP/0xd3c3wpWpa+FxGUdKiTevXzGMgzwTdg6JE0+dZdOw7Do2mxtplVGQgpJbZY2V57XrRK4eAj/60Y+IMfIbv/EbhRSuqVwlxcjMJSkFQMypfC3P56nDkO+5XvOmaJw8b2/v+MlPfsqrV6/YbeX9TlcuRfaHDcd+IEwejUFjyEimVggPVVRlmixThykoB2XhznouWNRpjhEjy+KKnSH1E3pUbPuRWS0284JUCW91IqxBaS1fYNBZZOLWiIfQnErhw5xTlEl4Yh7vWy+o4kViiPn+MwNo22L0GbUBmxWrbomzjrZqSDHhzcg0BsbJy3MLaC2FYX88oIwpgZWWqnISDZKyuP5ai6kqUk7040iYPH4Ycc5gjOGwFwdjV0sZIPOQwRqHhNZFiIHZ/bhMvwStCD5IUKdR+CkQY2boxRMqhEk2YU2FMaKOnHwWmdcvefwKFyjqa76al/2fuep/9fd/To2ivvLFH/krX/ODD0hg6sHf7xZIX/OiuSzWMQTRmkeJo1cqY7XkUNS1QyuF0zD5AaUT/TAQc6KqKipXnVIoJVpbpLHO2bL7g9kkC6DtlizPzvngw29xdn7J++9/TNMsaFrZSVtd8lKMISdDiIqQxNkxluCpe3Nqmbbn/5WCTJ2Klgz3acrvXjotrrIoaVv1vSeETN/LyxgrRUpVixonzu+fKI6uWZJ0gzyY4rswcjwGCQfsOpkomjWVzVQ6smwdKrdFkqy4Xlu6FqpGEXOk9xMRTUAxRUWIhv0+433keBB31hil3xtiwLqMK94FOSf6/kjOicU4QOWhuv+8kkSq6dqacYq83e05HBI+BQY/MoaJum5xVc1x6KmqiuXyHKUtOTtCHJh8Txo8oai2yFl2h0bIu5L6G6Vg0YlqGtEGaamQUUoTo+yqtps9cQpM01T4v7Jji35C60S3EIVOCJ4YEjkpunZJ17UsV+c0lSGr4iuiNG3lCU3G68cpLbMzqXqAMKo8T/i6cCKKU66fCkCRyaaUBGV8HAeJC3a25uo4FBPCiNEaP02Sjq0QU0Jbo7Olth06S5pojuItEWspSHQVhSf2wOU4F0LqyxcvOBwOWCc38OOPPuKEZiThhHkvFumjnwS9fDi2y2eO3jMOPXe3N2y3d4QQaJuGtmkLATqw3+/o+yOLxeKEFuVyLhIpIQTfcRxObd4QIinlIkMXs7+h7/GT59Xr1ywWCz755BO0dgWJtQi9ZzbLExwrxnyPer7TipYMqly+zozTxH5/4M3NDdvtlmF4LDMWxZYnp3BvIogkuaeT3xOP54kyUozWRa0ocFaitHmyWDlQEOCc7jl4cQql+3DC2pmjBgny0taoe2m7NMtOvjVGSxvIZyUu0smWYigjDHkhgJ8ymPRczLxDiNYOrVusljTuuu3Eot9UJB1RWeGnnhC9CAZywllpyfT9UcjrVUWlLXVVMY2TII1lEClnZZMZo6Sel9BBpRDkMKeTIjNFJeaS2p42hTOBeyZhp6LUkvR1aSeelKE+SbsnJFRKZAfagC3ZTN/k+JUtUBRCQv3axe3n/Ov/q8e7lRP3rY0/qr2jUMKvyJbse9CKqmkxVlCDtm2p64rz1ZmYkVWKfhz49ItPefHyNdvdRoqRYnYkOSWSJtu2LRnY7bccjwf6scdog6tqvvMbv83V0+d897f+FFXdUNkalJFwwSC7BGMcSkeOQyw72Tl9U5fWVS595DLxKMkPMlqUNXOPeYbJHx7WaLpFzTg6jkZTVwZbifurD9IGiDEVK/vI4ZDxQQoUmSsyWRti1uz3nrqa2O6m066iP3qsSairmuWi4sn5gmUNF01kCgtx6A2ym2ldwGZgbNA605iA0h7URFaRROS86vExsGsmDsfEly8DwxjpewlXVGiCBz8lbrc9znmOQ8fy8pyL9+6HhjUGpxyXZytSgp988YaoFEMKuK5hsVpy8fSas+WC/c0dfb9nt91TNR3rsycCw0Y4HgeG8cj6bI11lhAmaU8ZC7VB67Y4+iViing/st3eFfdWSaKuXIOfPJth4O2bV1RWS2iY0xz8AedgvV7z2eev+fGPf8o4BlzVkqJmmjK7XU/uatZNyzR5wniEHGmcFkTp0R1/QBxX6kEpq+ialtViyeXFJcPQM5YiRDhYohJDSZEiyp4KW1Qxi25RUAGoqxoNTF7Mw3bbHTkqSIa+H4uZn8U6jdIZVweycWgbMSlKcaaUJCMfj/zv/+x/Y7PZsFqvSTHwu//G70Cx6p/6A34a2Wy39McD+8Oefhx4OFNpJX4e+/2Gu81b9ocd3ntWiyXr1RkXZxfc3b3l9esdw3hEaXj67DlaKbbbLYvFgtVyyXp9xtX1E27v3uIqCeh0VVU8SBLL5ZqLiytGL+ZswzDwz/7ZP+PN69e8//77rNdr1uuVbILy3G4rxqDkoty7f67kds0tlrl4MHgfePXqNV988QWf/uSH3N3eEP34SL20WHT83u/92WKj7jnsjuy2B263e47DxGK5EMVKaVFLq0Teo6odzphTwOkwetmUxHxKOTaFNCxI8P21zrnMtSrfn74txFlXi3zWWkEz/ETIoIj4KEVNSOpkGijCxnyavmcLCFUKlZikTfTwiEE2LqYo6sbjhNeRUJfgxarC+Ij1CR2LH4+Sttxmv8e6imhr3OgZzDi/G94fpW325kY2eSlLdEHXlRaoJjtX2qJiPKjrWkwyKyt+V7604rPMoTkHgt8BEaUjdX1O1ZwLgh9iuVaKqhgb+lAqPRIhfbN19leqQPlF2fX3yMc3L07+lcuar7xA2d48PKefd34KYXFTQapkF1q3uMpSN5a2a0qBsqKuKrrW0Q9Htscl29mo65Q6KgFStmpwzlHVjQTyDaMs7CiMq6iahrPzCy4ur1itz7HGMsuNKb3iBDKDJUEu5GOoYr9dTLYUJ7mmLtW80opk8qlnKpb36Wt2HAprdXGslQuhmDcM6sTLSSkTQ8L7LIVLkhh1VQh4ko8TOPQTu/0otu42YU3Gmky/CBgTmKZYTKcSznDaTcUEforC2xCHKHBFDlzkf0ZrMAGDYnKBSRdGvY9MYywtnMw4ZqYxsd9PWJux3YRpAxcPPrcuk0pdOerKYrRItCNQG0vdyP2uK0dvIAXpN8cwEYIk8orEVLI8rBFpao7ixJtTRCHFqgAVRQWSEn7yaG2IMZzUITGIQmroDwz9kbY9EzJeloJCa12Qo4GUZ76CZIWkKBb/OUsS9vFwADRZFa7Gg2GfCifh/nG4L1CsFbdhV4nC56Gp37st79ny2zmLNQXhQ0y7JCBNl167IkaJGxjHARADOIUjZYufJlAKM03oJPERci1NiZb3bDd3bDYbrLXSdpr5YTGcJMBT4Qz44B8hCQ/OWAznxuF0DepGuD3OOUIIHI8HlNJUlaAgOd/zX2ZkpGlaFoslKSWaVmIlYiFZimS8ZbFcUm82eO+5ffsWZy23t7fiTLtcFnPDB+jJTKUp6Oa73jX3iKj8XIyR/X5f/myZpuEewSuHc5azJ5clRsOzbaTVmsgYq2m7RvgVStQhh2EUuWtKNE2Fs5bB+sIxy+UeJlAi2515QynIOeoyx5iSJK4MRWSQiFkBmq5ppVVeWpvTqFEqoJQt7qiJKczGj3Nn/h7ZyXMvrOBLKatHKrX5+uSUC1I42xJI1ARZ5pAZKT+5shbURxkDSgsqnBMq5VOGVC5iiWkU12itDBQn5NlETesic2VWMul7Gb1cAsn3OSmplDB+coDosTmS5me6cAnVzPvi3pZijiL4Jqvmr1SB8v9Ph1KK58+vqds1ikli4ldrFsuGs/MFrjKSGZJkAbc6cThWHKZzhvHI+fkZoR/oS0FgneW9955SNw1td8Z2u+P27oByDW3dcnl5xfrsjPc++oir66ecna3LQNSioc/SKsmzJ0qBu0G+ngd+yrPK5f5zyLqTC69EMadSa62L4uZBkaIy2hRX2xg4HjOqVwxDwIfEfvSFeCsujinm05+ZLJdzJmQ4HAZuNopPv7zhbNWybBuuzzvaxhHCRFU5Xr3dUxlFZxS2kvNLIZJiZL/dkUIUB1QtMe7LZc1q1VBXFmssWgk5TfmI8h6TjigfCIPndhPYHSPHHvGk6QesqxnVCt1OvH//oalrR6sbusay6mqePbumj5lD0pyfrzk7X9NWGqsmnj87gwTTpBnGyM3N69Oi8uz5M87PV1xfLzEG3r5+zXHfs32zwVadJCE7g3KWRJT8oJBwKVHVjhQjm+0dxBGVJjab17SN4fLiCqNlR+YnSXPujwE/QWWFk5S1kFcrpbAZYky8fH3DD/7wB1xdXbJarlDLBcpKNk2MkePxeMrCeZhWPjvuNm2Ds7Lobnc7SWHlUacQEK+otq64vryiaxtyShwHKdxyTmRtcHWDQmOMI+XMoT8wTqOYnYUFVeVQVmGHkaOfyptouralaerSajwwDj05Bq4uzjhbLbA6MXqPnwb6XpCeYy8pvZI385iXQErkGNhtbtlvN6yWZzhnub66RrhniX7o2dxt+N5v/ynOzs5QWjNNI744GZ+fn7Ncrui6Je+//xHjNNJ1HTFEXr9+I0hp13J1fUXTtSgUt7e3fP7ZZ2w2G/6X/+V/4ZNPPuHi8kp8lR5xxeRvreV5Dumerj+3ZmSTgaSbH4786Ec/5rPPPuXu9o20tN7pYFtjeHZ1UXgvtkRuJL58+ZLd/sDlxRlNXXG+XtAPI1++fMNmt+ft7V0JcHTsDkfGybM7DPL37igZMs6enJOnYSSnRNs2NE3N06dXkhXVyAau73txhE6Zp0+eYa1j7GVOUTlT17IBDCW3ZrOV99zuDoDC2KqEj46lfSfz0uwVld5RtKTyf0pJ8b9ariBrht2RpDMmK4b+wP5wJAwDOUXarsK6io++9YnwXqZIDsIXSbPkuuzamqaSexTECypMw2kD6JxDGSNtXRIqHjEWbJLCyFUao8UHKquWmCO5z/gwMkx7VDIi8Z49scoGQWt72jCiIMzF2zc4fuULlH+NDZxf/vglEB2lFIuuYbFa4FxD5Szr5ZJ2UbNatUWlAynIg2WNwlWWxaJjtVpxeXHB8W7DVt9X7cqIrXlVtzRtYrE6wzmHrSouri5ZrdYs12u6xYKmbYpM0zC71IYpCbcjpJN1/cmuHk7oxrwTg3soOJNKnzafFiJBO8KjHVpV1VxcP0NM4dxJNjmOotI5jpO8pirSvoLkpDRf3lxIfDBJGhsoWHY1XV2xWjU0tZWkZ2swVYN1mqoyVLWhqkTml1Mm61b4FeX0tE6YugJbg7Fko0F3KAJGt1R5YrnuUS6C86gq0vSRKQipMUwjxlasz55xdn726H7rIh91Tuzrq8rKDtEjpltNzbKrWTSW1orp2Zs3e+YU49mPYE4p1UbaYjpFVAykaSJpcbEM5TK5IiXVSgpR8dNJaJ1xxmBVTYqTJEVHuW9i5qQYBg9oqqohYImoe4UNkFOkPw7s9wduN1uxxzeWrktfcVCeZcHvFiiz2ivEeEIlpOCYx9Q9sdIYi7OWpq7QSuG9ZxxHgaGzeDqrokrIGHQOUkyniI4GOwpHx/UOEzw6TLLj1OKTIWaAGT+OdG2LMZrz8zPatiaGgmR5+eO9ICc+SPBmio9n8JTiSfqstWa9PqOuKtZnZ/SHA5vNnUD2zhUzuRV3242ETFpHVdWiQKoqQV7qphitNQQjPyOZR4qmbXF1Q98PaG148eULfJCWz3K55LDfUzU1rq4pcAnzrJrybNz42Cog50w2mpyg70cO+wOb21sO+72MxZS+gnYrJVwSq2XDYqxw6FaLFq0yXeNoakvXVFitGM+XKMT9um4c1jmU6ogpc35xSUyZYyHAinGjhN+FaTrxKOrasVrU4lDcVSw6RwgdQy+o1dX1OcZYhn6CLAngVW1oaov3nhgCq2XLNAUOx5XgRtoyDOI5049jESR4Qkj0g6RrP/7goFQ6SeinaURl4RepnAkRyBGtxYwqxUAKooSbm0WqRB0IYiNatzmNXakZH8nMwoR3mQWqoNHlV4QorGaZu3BpsnLi7GtqMpoqAxj8NGGI6JyKQq9Y5av7zaCI5tQ3Wox/5QuUX9VDKbi8WnF5fcl67aicoa0rjJUYbTHSKvBxKVCqynB9fUnOlhAs42HPzcsXkoDrg1S6ytK2Z1T1iro7Y3W2Znl+xnK1om1bnr3/PqvliosLeXhnNUGMmXHweB/FnTaWyO5TZo3ArszM9geQsQziWFjq98FwAOSImS2TgbOLaz5673scDzv2+x3aSHqxL8Zv0zSV1ykOmNZCmhtB96tAyopQkJ80ky+ZPQMySgsMXDlDV1vOVy1t42gaV7gQInkOITENkk8Uvb8n3GkjrQMrD2adAzYmqkuPjxnvUyHvQt1I9kbOk6hl7IpDn7jd3O+sdfGt6Nqa0SeaumLMgTh66qricrXio/fOuVhLrHt/HPj8i1f0w8QUilQ3G25vNwxjz5MnS2pbUeeEi4E09CRtSHGiHz0+RJ4/fZ+u7TBWnDn7/khVGdrOcbZcsm5rYhrZbG4YeoG+62aBUhu22x6lHFeXT7jdH+knmZwrp3Ea0jTyerfjxYtXfP7lSxIwhMgH6/dpq3mMi5RyDo68l+wWODxnfAgc9oeTG+qMoKScRa0RIyhN21Qs2oblokUB+/2ew+FQEq3F/6dpJKLBWSkMRLUju82QAs45fBK5rk/+lHXUL+XZUAqmyfPB+++hteI3v/sJ5+fn9P2eYRgYh4FjfxD31v4oxNRxIPrHVvfTNND3B5SCtqn54P0PpOhYLPj0Jz/kxYvPySTOz895+uw9lqs1P/3iC6ZpouuWrNcXXF2LBxFoum5FzpKa7b2nWyyYvCfExPnFBRcXl5ydnXN3d8dnn33G3d0dv//7v4/3nt/6re9xeX3N9dMnD0Lf5B7EGIvHSLx/ZmeSqhKE7PWr17z48gs+/cmP2GxuidGf7t9X5rScSSHhw4hzFVUlBUTlQBNRKYPvqY3mg6cXVAZSGCSVVxsu1iuqpuVbv/GbIql3NcfDgbvbG3bbLYe9hGimGHn98iVKwdmqoqlrlouOxaJj0bXEJMZjTVehlGKaAhqNM+JY7awUEDK3lbRlXZESjD5xOPRstjsOx4FhnLjd7OiHkZdvbunKOJkPoxKowGEcCTEyHWPh8TUkk1EqgUq0jSEOIgUOvSd7j6tFuRhHf4IMfZTMMWeqMmcISqMIJ0RDnYi/clgnHCpR0GUgljuchcwcEkl5tK5ouzOqSrHoYL+7Y7e7pa1sMXI0p3YpOZewylLEWvUgY+YXP34lCpRXr17xj//xP/7XfRp/4sdPf/rT09c5JV5+8RP22xuaRnJiKmtQSotBlo+FdyC7M2eF03HoJ/aHnmG/Z9kqPvzwiraRGPqLq8sSv94WIlolfe9WU9URZ0b88TWHsAW/kUlcPUBQQiwISpS2T4pFvRPvK+lTUZIe7MhA5YRC+qwPK21FQj2Q5nkfud0cmCbP6BU6ygM19zljmrn4mpw0KegHtIb7F84ZYuHNPOQt5IL8cEKAVAkeDLhjxrpw0iv4kEgFMRLHxoetK+nY6vJQZoQMF6IRRVHU5b0hTboUKEVm7TPT9KCvD2yGmsFoen1FaibOnnTUIdGOkcvzNa5eM/iWzdHRT4bJVzSr51BFbCf7ppw1VSUkuCE48qDx9gy9aLh4VmOqGtsuqFshGjeNOAprIy26MHGyxM7BMxzkdY0JoP8FzjlQB3abDa83kYM3JNdSdQbqiHPCewlai+tlVbO+eo9v/6ZhvV7Tdd1J+QLQti3f/va3cc7Rdd09qgZlB9xQuYqYRHn19OkTuq7lyZNrmSwLx8oYzbKtWS6X+KzJPuDpGUMgzLv/DISITpkpciJ2z396H9DGsO2PoCDmWBAjTV1vcK5CIYv2oR9RSvHpZy94dXPHl69vJBnce8ZpJPggkH3wjMeBcXrsjbHbSkL1NBzl9fZbRmMZhgPTOLBaLgUpIjOOPZBp6gpnNM5avJ/48ssvxJ7fuhMvRJQ/kd1+R18s8Y+HA+TMcDySY+SDD97n8uKCunKsVivevHkj7aTN3alAEdMvTp5KKZ8EvfcMIS1qtc3dluNhx9XlBV3XcLZene7fortP7I4Rbvfl66SwRoI/x0kTgkNh0QqmJJwRVOYw1WR7TirWBCFXEBw3b3usDWgzMI0jx2NinAwpNwKgqkTdXaHIZNXgs+E4VUQ0o8+lDaNxQz6dmwKMFvTQmIIKp1mtBGhBUkNITJPGxwa0wdiablHhqoDSS5yrHxVnWTmUXmBdhTYJrYQ4r7UDDZFENhLX4BYOXQWMkg0QTj6PVu7U13GV3BPxQpmRDCHIKm1QxhWzRGRDUlpBSRWuCQCGkBQqQ8CRVCKXkMAxjPftegW2qsAYUml3prllj7wmpsyh35DvqfI3zUH+13hst1vOzs74T//T/5S6rv91n86vj18fvz5+ffz6+PXx6+MXOMZx5G/9rb/FZrNhvV7/3J/VP/e//vr49fHr49fHr49fH78+fn38azh+JVo8MxHq/hvv/Pc/nnf5mu/9fNhqRrX+uDCqmSw4HyeiIP8q7zMTVvNJFjdDwuVdHyrnTqTWX0TSnd85qUy+f5389QZsX3c8hPbLCfJ11/6+mfLw31/7Qw9O8Bc8ftbH/Vmv8e71efB5fxGwMz/w/PjFzu2hUda7v/c1J/nw+quf97vvfqtkk/zsn/6Zx1fBWhlYeSbw5a++3gxTz7+f37kqp/bM173fH3l+6tFtOr3MV87zZ4y3k1wif+2z9+gZuWeFM9sJPExGfjdz6vT7j+7NNz3+hEDy/LVf/uK//s5nntu+v/BMW67hfYv28dz06PVPrcH51b5uBL/7vfvW79efxx9xAdT817sE0YdPWzq1x37W8ZXRWL5xeg5/xq8/VLI9nq8fz8NfJ844fesXm0pOf+c8++Q8Ph7P4b/48StRoNx8+VN+8gf/XC6qgmGSvIHaOWGSD6Ho2yO5OA6GlESTXpIWQ8hFmaXkZ6P0mEW/PhM6Hy5+MuhMCWVqmhptFCF6tFY0tXiTaKU5HnuGYcA5ybLQxpByZhz9iQVfVRVVJf13OR8huI7jWEil8Nu//af4/vf/LQCstfz7/8F/wJPrJ1BcG2PJeRAug1yMmT09n3oqPztzRkJI3N3ccTwcefGTH3M87Ni8/YIcAioE2qqiqSoJN8sJH4TQ17Ri6lZV9UkFlOcRqmTCHcaeKXh2xz3eewY/MfQD4zjx8uaWQz+yGwbJw3lYaFHMn7Kirmr+/L/35zk7OwNAH/bYV5+TrCPZirks7ZqMy5F2t8HmQKU8VeHlGKPQRqTU9+ogJMgslayS8qQbYx/JKqXnjnB75gKxmCr5aSKGyDhMhXMjvgtoTf3kCXa5IMdI9J7jq9ekEEg+yrlYXVwbFSEW65icMErRWcNNc82Plt86XZOkxAiqqtxXrpW1Dmcr6naFtTXZCjlSB48iYVQUr4KUSy5LYOi3QvRTCmMd7XKNMQ7nasoqWuoejak6lHHopkHXDWa5JJJIRJypMdqWa5WI01j4RGUR1uLAmVNic/uGGDxGG7R12HbJcNxy3N3w9sWnHO5uWNFii45n0Vo+eb7C1A2m7Xh5+1Nud69oJEsbHy0pZbyfyERQE0lBVIrjzuOnhA7yDHZNBTkR44QxDmMq2m6Jq2qUFSWbL6Z1lGwqP02Mw4D3E3VRNLVtA0qRlTi11k3DNEqYnp/E0VnC3gyLRSt5MSnA0cPRM32+IR8mrp89p12vOP/2R7ydPP/Hq5vTpD6pTNKab3/8kZA321ZMu4qyQgEqizW7Lk9AyMUXJ88l32mAQ06PFtl3F/R5zpk3KRkgPXieud9cPNpulP9+KjAfFpvweP0uycMhK4aYGSP0tzvCIPPJNA58+cVPmKaJYZyErwbYLDB/62YieXnis2J5dsHF1VNu9j27YeTubkuMmcvrS1KObPcbGcdKUbWiRGpWC4xzjFGC9ayyD9Zg8Rhxhb8UfSKFyDCMiLKlpManRB4ncpCsLZUy5EIULmoWUSWJAdvFxZqmqVgsK7JyHMOSeeV/bn6fa/MjuatK3Iph9lVRJKUJUaS6h2Nm8pl+8qAyTSsBg/tdQpXQwWmaSDFy0SUWTebjDy2LVcX1B2cnAcC02eMPA8e7I3GKYhipFFgjAbG1puosrrM0KyeRIuXmpymVvB51UqTt956+T3z2ZebNLfzf/2nkOBqm5MSY0yT+9O/8GT764Nv8ssevRIEyDkc2r78oEwccRjHxaZwjp8z+KAVKSIFkpEDxSRjGOQtZ0U9zCJnY+IYQT4vZbAD10I/jYSKq1oZu0aKNJoQRY0QCbLWY/Gy3Ow6HQ4lr12jrSClz6CVcKZ10+c2JRDpNIzEGjsehvDc8e/b89P5aa957/h4fvP8BcRS5op8mTuFaSp8Wy3mHLdVtLn8S0xTwPpBHMNHwVjlCVpgQUSFiYqZNsMhapJIpkMeBjOTdmOiwhTx1KuKQtT3ljPYjTBPxKO6Z4zhwPBw5Hgdef/mS7eHI28NRsnkeHCeuVlY0TYuf7q3AVfTow4Zc1UISK7tga8HlQNNvcMnTMNBYTesMxmnJhMgV2hRDrpwl8T5JYFsqE6y17iTRm89Dcl/upa4YIQmOU0/wgaHcxxwzJaCCOnc4Y0kqEZPHxQNxGon9gHEa11gscnsmfx+KaJVimTSDbb96TXQ+JV2eyKKAcQbrLHVT46qWbJfMBYomYVWJGoiZFHwpUgZiVAS0WGW3Hc7WVFXHnMlMSSM19RJlK+xiiekWVOeXRCWuvbVrpUBRShb28Qg5y0StFcpoMXdKiWANwU9YbdGuwi3WqG2FVxPprWFS8RGSYLXirLW4RY1dr9lNht3osdmjcyapCpUyMY1kAqhB7M4VJFVC0aKkuuQk7r8xjihVY3TAmFq8IFyR2iMZNcSITh6lRzJHchrJyYEycJJTKqwVlY0moonkOJFIWJWxxoqjJpkUPTCh4kQ6bknbkWq1oq4dSwP9O412ZQzaGdYXa87Xa84WreQOKYoRHqgcUDlB8pAzURkSiihU8zKPxAdFygNpf/nidK1PxQknyXBO8+/co1vvFin59LribDT//W6BkhHvlpjBo9ABCJlxdzx95pQSh/1WFE7H4aSuczljgNRWxQ12tpDXOOuYlmsOhz13+543bzcSWLnoSCnydlMKFK1oUkeVIqF1GA1DkFgEq1Oh1MPs0FQri86KGGQdOA49uaQA65wxKZH7ASaPCuG+QJnHvRLkz2opUtadIVuRz2f1WE/Y6i3n+gvBUZSMeRRErUlZ7ueUYUoZn8TPyYQJFFRYqUjHSFaWpCxpHIXQ7xJNBZeV5WxR895VCRjVmdFsmcyR7bjD40XhpxTKWpRTUCuaRUW9qlhetlSNEOVJEMco00JSTOPEOI1srOfoIrvbzF5nNreezUGznyrQCWUT3/329/gmx69EgVJbw3lXE+YclKTIPjJME+J6XXa9SVIZMzKBYIS5HGPGMxa/jFgKl/uC5N0dx8M2SyyFTt/3hfmfSEkxDIq2kZ1bVTmmyd67eRbr9bE4tcr7KHKai6GSlxOj7MqKAsaHx9kV4ThwvLnj8//zX3Dc7bh7+6YgJkZWPq0xdYUyBluJ+VWY8zRSJvQ9YRzZvH7NeDwybm4xMfFhY1jWK56eXVJrTaUUL25esD1seDFuGf3I5tYTgaiVLMraYEseizYS4idFYWScRpE1Bk8IJZY7ZuZt0sPy5CEIKNlA6Z1vJrT1RKuIlULJLIBatZAcw/6cEAZUTKgsEes6+1I0ideCeFfce8SofO/pkFI4IXFiHGdPsHCK4gQbvdyz425fJjFBTsTq2WHrmqPVbAywvEKjWdbnxO2Gw09+gF23VE9WkD3kSD5MkKA5X6FjIrzZyHV9cFhjyLqMM6Woa1EDGC1+CEolYjyAn1BBnFlTNoIAqhKWFtPJw2WxWMpEqcVTIWWZ9HPS+DgQ48g+3BHSRIgZayvOVte0yzNUGlCuRbsGP0DQhqouqdmRgvNGsBZlDEY7slVcPf8IUkYHuedRKaZqA1WGWkGjYVRzYDF5ioRXW6rriu6ioabCRsN+vyF5j6qXMrGW7JOIJ2UL2rBsl+TKMG5HUoj0h54UAz4MVFUkVpmqHlFGPGMkUK0nJTG9csZS15LFE40XY64xcbffYbQEanY0rM46VosOOtjaLdMkBl3JD2w34piaUiQNgRQCg5sI9cTnL3+Aemvp+s+gWZNX759G/5OnV6wuz/jetz/gfLmgywFiIk9e0NSYJagQxe3tG4IfOVuvBUmrxKdCHi1XxkFxfJ1t3h8UJicfotLeTaXISAIvF5R5Ro/fKVQeFCbMBcqpELr/e379pDReG7ZRsYuZ4WbLvcA6n1DIGCOTD0ze45Ci/XxRUzlb5gWNMTXj1PPpT3/Ci9sdd4dB1D9OChlAXJIRdNL4iHZBUplTxhdzvKwkRFGRy3MEIYhST1SJiWka5R7GgNOaSit0jiiVUCqCzqh0L63NQFKZkAKKzOQTVQAfFOjH850xiqrSHMdACJkQRd7edoYpwPGQmGJgilGQ1gxVJT+zWNQ4B37K7A8T292epoGmhW5pWXQKVyeU9oyHrcj86wrbNSjnsFMm6IHD6z05JEzWZJeJdWY4OKqtwwaLXjkWTxy6gdSNkBQqGuxkcVNHvRYr/OZJ5up14H/9yR1fvPT84CcH8ePSRlKZv8HxK1GgGKNp65qoNBHF4BMpe4HxSgWtcol8Lg+WzAX5gbRKwvLebQV/HRz68Jh/O6XZ4jvft1hKkfPwNaRlcG9W9rDdMil/+vngJfl3LpYe0ULk1BkPR47acvviBf1+x35zd9r3hhSJOVHKZowz4k0wJwlnSONI9CPHzQY/DoShx6Co2iW6qqmNxiKwZUqeUGzTgx+luMoJD8xBOnO7y1gJLZsX/VAMqXKUXoYin3YZSlFSUO/DwGZmglEzrP3weucyKSQwmWwzymaSzkSlUVUtD4OqyARS9kU6nIuJF5hyLefrqh8UKLlMMNqII6YpxaHEtsu9iLno+6N8r6QKSUtPiyX9HI4WYiPthHpBbgO5duSugfUSfE8OEyYJcG/OFjAFwt2OqB9vq+95GmUspSQmdQ+uWyqeN0qJbb1WtqCKRsZRQetyRiYNXWLgsxQwpHQydvNxwocJn0bxViCS4kCaamJ/QAUgKLKKoIwEvGl5nbnoU8hzl5XsUY2pBNjL4lkTUyitw4C2GlM5lH9QoMRE2g+oVcBisMpitSNHMWfTKYpXytxakLuAUY52cY5VNcd8JEwT43FHNobKaayrca4u5yvIapw9dEropkaRjZPWXslMAlAl7XXZrlk0S1rXivFa9DJ20j2n4IRQaE02muQ0qTHEoCUCIHqOx1uc0ixX9/e6spamqmispjFQx0QmEuJEDBKVYJ3IbZMfiX7ARDE0S2OQTVdWWNegbX1iOWRVBr6aL/DcyCvzYM7SPMuZWOYoPc99D9EUihkb+ivz48N5TUbt3F9WRCVxBhaFVSUn58GTPaeZzxvAnFMZe5SgvCwbDCUI5+QD+3F25x3RpgIrCFc6cVOKgaBWkjmmNEYpQp75TPPzc79TkrkcYkgnPycZrxFDJhdrBbmWnEIjMyLBnou1WaI+t/VP6dePdmT3PJqUCjqXxaAtBLEwCDETYjqtVTOyH3wiePGbgYy1GluBq6SIqWpF02rq2mCd2PprkKwhragWNRmFP0yEMRKH4hw9SlsmE+kPEaUCdAblwFNuZwI/KaZJEGAf4BgVQVnahaVdZFwVycaiisT5mxx/4gXK3/pbf4u/+Tf/Jv/hf/gf8l/+l/8lAMMw8J/8J/8J//1//98zjiN/4S/8Bf6r/+q/4tmzZ9/oPdq64friEmVlMc76jt1x4DZEgopkI2mkwtUIBfqVyTxG4SMI4VSXNkh6VFg8PB56IwDiJjkvoQWZUAacq8jA8XiUvmC6L1aMkUdCKYtCHA6HfmI4OR/OgWc/+8g58fqnn3I0lh/9b/+UOI0CeYaJYRp4u7lhf9hy7A+EGAip8FNOy70WPX0BQOYHqbIV0/qaHEbO2q44F3pebW/YHDbsjjvpE/cDMcVTaFdS+eQdcUpVVZLrErP89wQQ5SGf+SFaKfSD5aVYHKBQ1EbTOfdoIlMkTPZoY6GCXGWiyfRELNCul5hQo7wij3vC6E/5MNlHydSxrrwvgOT0hJInMaRAzJL/YY34vMyuipKAGx5g3BKGZjWlMLMYq9Emc/viNS8mz2t9g21XNP/mn6FaauLlGv3snPjJU3h7C/s99UoM5cx7T/GHnu3NHeM7D3SKkfQQTYqSIGu0KfmLGT/IODNaiclcVaGVJmlbkL6EL2OwqlZSpCB8KGIgZik6+/HA4HuG7BFcwpOzwqpIno5MN2+I+kgyLVlLZGlYLzDW4eqmLAa6WGAjBZBSaBzkRPTirOqnA77fE8YjtnZ0Z2vMGKThDuTJ499uoTujzoaFbVk3K47qDWPMxODRRlGVAMqYDdYssG7Fdz7+U5wtL9ne3DEcD7x+8SnGZBZLd2IPHvqR0XvxFwmBY3+ELPkjVAmddSmWNGiHUbBYtKwXK37zW9+hdsLP+uLF52zevGXX7wjRU3UVyhoZC9qgnTu5x2oSaqkZ304MwbP3t6x8xYL7W1spRacU1g+YMWLjSJwmpu2WfpjYHwbaZUdVVwyHjQQ+tg7fZzZ3d8QSTHd+/R6riyeSAVPmDFlU5wH8kMt0/7cgAPepyKV2Of3MvS/0/D11j8SUgieVPsbDGfRhhrl+d8elwFiLdY6qioQYGSY4Dj3Jey7WCyCzbKTg8j6w3R95s92zH4KYF7YGRcaa0gdNQSzbjWLd1qyWHXVbSwstSAtQqvUysxQ0PJQifvJyHuM0FWQ9oK3DKQU5iXuqVhg0VtXklPEJcpZsq6oW9LxdLmgXHXXTkrJFDY9psRkIPhJ8pGo0KWZ2m4CPicEnfEz4QkfQSqO0IWV4/fLA5BPHY6Jpa66eLKiqjHWZ1VlivVQ8fd7RLSztWUWOmeQjRhuyVlx/eEkKid2ypd8PvPr8Fj8kDofM5BWmz8R8xDYBtVF4pbmdsiDnwDgmxjFzezcyDInVWUVIiuq8ZRkbVrcLsm5RZomrlnyT40+0QPkn/+Sf8N/8N/8N3//+9x99/z/+j/9j/sE/+Af8nb/zdzg7O+Ov/bW/xl/8i3+R/+l/+p++0fv4EDgee1brpThG1jUxJm4pSZMxEZMYAJWNorQxT4/LTILkQfV+P4RyLoUJnPr/j4oUpU6Ok0rLUjtNk8wDqQR4lUjr08+d5gbhNswPh1TJfzQclnPmsNmI2iMHmXS0pGpOYcT7keAFIYkhEGIpUOb+BWK0oxRoI+dV1xalLFXVoo1lDBPT2DOOPfvjgb7v8dMkSZdR0llzjCSk+JidCrPOpbUg75lyBq1RRuO0QTlD29TEnKn7AU0gRCkiDOqUm9HWlrZ+XKCECIc+ManAqAayN8IJcZC0lahykAwiXUiFuoT5aeHlnKLhFZBVCfxDUAcjI2JuCQ0+3NcExfhOI9dLgrfK/FYiA3JOqBRxMVLHRBx7YjJsDkcaFYTr0bRo26KqHlV5SQ4uBUYIibRoyenesOw0UnLZrUEJDVOlKJZdv2zopAWQlCKEOVo9lR2gTHaJjHMjKUesclJEpihdQSVwrFZglUZhpa2mFKMfmYgMaSKbHkxNyJC1Qbn3sFUj11MrMKBsQoVELEZOBiWTuu9J08h02BD6nhwSOiuMssCDXJqUUVNA+4RJUBtHW7fUVUOMnnBqvwW0stS6Ybl+wmL1lOvL91kvzmn0gv6wxw9Hcp4wxjMFL89HCOQUMSicUlgkedtZV+zg7WksVAuxi788v2LVrTg/u2bGVYLP7A89/TQRc0DVljmhWykhemYFygi5NhEJORBywOckSGeeB5IMVZMzDEdi0mwOd6SUxOlZK1wrjreaTFtVZKtxlfDaTNUUCF7jh5H92xuUNSitMW3DbLLG/cx3/8ZlPM1kUJkj5x+TZykr4VnMyIgE3CVkmzH/jBTM9+XOXAeUDczcTnk0oXHivejShtVaE1PGx8ju2JOSRCxkFFPKDD4w+nDabM6obEoRyDR1TWUNXVOzahsWdYVRgppVJRhQCPkyEQpBurSashhMxhK6d79pnS3ly71SBqMNbb2Q57JpAEEd6rrCVY66mblvj9u2QCGvi7V/RsJTsypBrqVtpLWgPgKUSIq07F9NuXRynn4K+P8PeX/yY9uW3/Win1HNahVR7OLUeTJt0gUYPT1L9z7cBfdoukkL0UQIyR1EEwmJJh3cQoge4l9ACAnRAQkZ6QECjA3GmXnynF1GxCpmMcrb+I25IvbJpHBe856Ud0rnxN6x916x5ppzjvH7fX/fIgiZty8RkmZZelwLbTbkGAmLEMBzyui6mjSNIw/Q7LeMKXB/nsmjXGszZ1STmVjwRXMMWXheNhKCIgTF+WwJQbEZe1LRPJwHpqixfUNRHageZfqfOPf/leN/W4FyOp34S3/pL/H3//7f52//7b99+f7DwwP/4B/8A/7RP/pH/Pk//+cB+If/8B/yq7/6q/yrf/Wv+HN/7s/9sX/WPC+8fX/Pdjtwtek5j1Ic/KhIWFUiEZMiJH0hI6JXPO8RU1BaYTFEPkQw1JOaQQqVb91otbCp+x9QGMdJ5pGXpFJ1yTapOPel8LHWEmN6kl9TX/R/UKiUUrh//Q0LYEypyghD8oU5LFUFFCkpQoqUqvC5rJpKXRQwqTpnSipoz257g3OGcZk4nu55ONzzcHyokfYzuSpTxE1x7acEmlSItTJqNX2Xr9rZCykR41hCwlrHYZyYUcxxkZEK0GjJnNkMHZtN/yS5WODE8b4QlpmweIp1YAyxDZL0e9uSdUYnxVqXYGxdpCtKk9crKA+hbB4ahaYx4q4YQyDnwmnyj+S/elWcljRha81libicaS4QEptc2Gco55F5ybx6d8d2MLzcXkG/x7oNpvOoXPDHIzEkzncngeOvr8nLAOfH663rmqjXXSNJl1dMrAnBwksQkrdseCmnyqdRletU8DFKsrMqOGtpXUspWkLGDBjtsAZcVigcWRlMnTYd5yM5ShigsQ5tHbOfySisa2nbHbkpWK3IBpS1FOMIiMOkJaPJ9GomziPj29f4fCbnhEoag0PhWWc8KhfMFARFiNC7jv2w59BvURRmK6iAiglrG1y359OPv8cnn/wSL28/Y+h2hOuJeTxBCozTA6fTa/wycjg+oJQBrWiVoGBJCRQ/bHcVVqw3UIbr/Q3DsOW73/0FNt2G2/a6jo5GFp95f39gyTNFZ0rXYKhjrVJIWoiTomiKxOzxacGnSESSaJ8euhRsznA64Am8/uYHYC3D849wTcew22FSxKTEzXaLUoVm2JByoU1WQjKDYj4+cHj9in7T4FrHtv0cbRsy+sndjNxVH9YoKCXFgrrgHuXpH8qf1VGOojoDP6ZxroOj2gNUnheypRrAPsFy64om61URS35ToyJiLkwh8fb+wNguNNaB1iy5cF48cwhy7xYuI+EUI1rD1W7Dpm24HgY2mw1t11aL/1Sfg0RCmrastSSNJyHzpiKFUUrpMl66KDkvzatCa1HQ7XfXOGtwFkkkd4mmaSVqY11pVbmsI+uxhi2Kw7MiJRn3NjVtWy2XKT0pRHLKhCWTssZoJ2iplqYxpsI0ZVLM+AfP6Urzy9/foq2j22nCDPMpMt2fCeeFpnVYZ7l6foVpOjal5e104qs7z2lOjD7j24JXmW/en1mCwmdRGjWDohQLOHLaQGlpuh1FW0ZvSaWj2T0j4yjFoc2Gn+X431ag/NW/+lf5i3/xL/Kbv/mbHxQov/u7v0sIgd/8zd+8fO9XfuVX+M53vsO//Jf/8mcqUGIunJbI3XnCHE5kbej6ntubW47jxKt3bwkJQsp1DGFQJFSdyZZSL34pF9Lr03jxNYrd1PnitzkRpaSq/VaoqgqK8TEeWzqHusnkAoiqQul6s9euRWsqOe1bx08W3lAKYTzjSsEpSyFL+FgQjkhKgZJkJBGjdBoFJRHdj1NXUNB1HU3TcLW/pm97colM88zRT5zPJ87nE/M8EUJFT3LG168hRRlJwSOysKJLShYzrQU56VyDbQesEyJf186cxonRTtynWEcWmrZ1l4RR19gPZbWlEHLCL5klF7KJgs50HbEtNL1HqcycIuSMKwqyjPhU7Zhirt2MWbu6iqqoOmoqRVAAQNfQs6cz5RUpSjk9Yd6r2p3WWTKZVkmCLrYhl8QSCnfnSNlAHxoKDm0c/jwT5xk2EWU0NFY2iCeHrnbeBun05rAAhlw0yliaVnKRlFLM00JM+ZIyu0aepyQljlaaXMTmXZdCyYoQQeOZy0yoox3TGZTOTPNZ5vBLwFiL61shQpuCUy0UUaw4rVEpPkq3g6EoQ1BWuAdW+OptI92uA5qi6bNDk4lKE5guhXNJmXieYVpofKTJGqdb/Bg5H0e8E1Siaxra4ZqPP/qSly8+5/ntS9p+QFuDUx1ZZ3bPXsBBczjfY9xA30tIXykZW+Phe93IdXRNVWM5dt2OoRl4cfMRQ7fhavccaxu0bohL5OQnPIHiSi14DEVbMhaLQWXQMUAl8pYkkm+KQqMYtKVV5oNnPKVAiAuptChj2d08J+TMOJ7R56MoW8YTepmJ8wnIdC9v0U2P6W6wqkE1ParVmKRpbMboRAozuWSUbS98D+Ge5MuacLnfeDL2eZxgV1RFvpEvCMy67eo6Eqojnyf/bv2bWYFWj7ytJ8uZ3KO5ot1J4hZEfqsJGZaYGRd5RqLWxEIdX8kdk0sil4RRBas1umnQuTCdjuRlZnL2srYn76XYENgcZQyq8qdyLjKWTlIQKJm5U1ImKeEKupywpWCcpesHvvenvs/QtQy9ZpkPjOO7y14SUy1u1NPi5nLmVJ0bFOGd5Czcrlw0bWdqwVQ/ziIpzylnGb2pQtt1hJDxvjZIQNs2dJ2R5iPLiFjXJlT1EEpDDIG4ZI5vTxSlaZLiulF8/knH1+8Ty30hlmf4PBDRJC12B8oqtLVo22JMB2wptCTfkbIhZEsqjkgv411UNQb44x//WwqUf/yP/zH/5t/8G/71v/7XP/Fn33zzDU3TcH19/cH3P/roI7755puf+nrLslxCwECs7p8eIRVGn7kfZ9TxxKbb0TQdV1fXoC1fff2GEDMhc+EJqOp/oqiKllAqF2FlpD/WuisPQVJdC5cOTyFdcxH2u8p14y/yepeuW8kDn+qGUYpk0zwWSDIXVuuq8GQ+q56uG08LlVKI80TMhaFpRW0W50tqqshJpQMIKUohsXbfSjjr65hjsI6m7dhu97RNQ86RZR45PLxjnmfmaRL1TZVfp5zxUWDFJQTWWbXVBqMqPFvft6rIktOWzrV0naSnFqVpneN+e8QomEaRHFpra6S5wKOionk88RV6DT7JjFhFitKoZEhR0W0XrIGZhK1zW3mSkc2hFBLrwmRkpEQFlgBTxB9HXcY49YpnLiFb67uJRR47U+8PtKYUIdaZUnAU+sZRnAQq+lB4CAk3w3WwKARNCtNCOJ1FFdA6VNej9LcLlMorQZMVTCmTNaQicLBrDK2zaGUIQXwkcpTxQSyZFKVAcbbBaE0uCXIklkQuCp8kc6gkKCZTDHRNBwaWccLHheV8ptv07K4MRUu3bEuLoZUgNa0EsSuJlOeL7DXqhqIstEq6r5odYkuhKYqcLZoiiyCPyF7JmTQuMHncIgoKqxrCHJlOC6HJaGdpuoGuv+Lliy94/uxjbm6eyxBBabTVFA27m1tCjmTVYFymKxD8TI6RzjmRkTfysxdVkNz5hudXz7ndPuPT55/TdxtohFiYUiFx5rzMeCK5AaUtaEXWEmCpikHlBMHXAkWeyVx3bFU0nXI0ynzwaOcshNhcWtCWzdUt0zJz9+4Vahkx0xHz8B59OjIf7ygk+vkTmt0N+89ajFMYO0AjBYqxBaUjKcxQClZb4c6pVfL6kx3QuhStoMmlSLlgj3zwq7V4yXVtKk+biierpqyFiqeDpsdnm4scflU8rqhvzAWfMpOPaGsoTpOKqvle8jNzDQnVCIrntCMtM/P5SKhjY/UEjUXVhk06w4p5Cfk9FUF0ci1aVuQkp0Ss5GGjQGlD2/Z88Z3vst8N7LeOu7sf8+rrmXEWzyfyExSqrrmPvxTkSdV9IEUp0DIaZRxN4wjyqNYCRZOSNCAYWV+bphFPo+gpWRrhpnU0rRWpcvV70lphmwbVG0xpOfsDwQfiOKK0pu06dk7xyUcN51B4PxZKuCHmHVnbqgbzYAzKtri2xzUDymwpquX03pLiWqAoIoqiMpIo9NO67P/58SdeoPzwhz/kr//1v84//af/lK7r/kRe8+/8nb/D3/pbf+u/++dKG7RruTtNPMwTnTngbMOzFx/TNi0xZu4eDnz9zWsoiZJUTe7VsggmSaHMlUl9eXbWh7Ju6BjDSjCr+1HtDh7hvwu5u3blrHyTJ0zt9YWNkY4jkzFFdvQ1cO4CuWqJvd9utgz9t7wxkoxZcpHAtGU64ecz0Y8S7Z4i2VhQBuNs3WjrKKIkuqala1qe3dwwdB2qJMI8cpzOeL9wHo9EL6jMUglj59lXAtsi3UFJH/Ax0NULoFSfGKTo6JqGTd/T9x1N29XZf2bTGFIQpjlaY6yj6VqatsE5ed8fzm5Vlc8WLBljFMVoSIrkC+PDTNHQKNmcbday2TSGbo7oJGz8rA1RNzQl0eeAcwprFDlX9VFVMzS1mFWYi7vres11nbmnWuiuioecC0vM+FhwQ8fQbbm62oNS+HkiZsPxPGPSCR0fKE7BpsVutuCMSIy/Na+22uC0oneOnBNLWMCKpDurTMwBW3TdAAxGK1rnZMyTPE0rHbOoVyxF1VFKkc9jXGaWsjCVmX63kYyrWbrclg6jDabNGOtIBQyGolo23TWtHmhth9aWkCMlR1ScUUoLj0UJ90LFKusPmRxDVeAYOhoZrenEpMxTFoqMRUomek931XO72fDixScoo7lbHjBtw/Pnn/Lyoy/49NMv2W2f0bW9JFUjyJfFsdnt8WFhu7tG5YgqkbCMpBjqBkHlwRha26ASqKi4Vhu2qqWxLca1YFpQYFwhPEReP7xjDAvKGpyrYZ1KoXImxUROnhDPxDATwszpPDP7wDwFYs6EAM7Ey+QEIOVISIGzlwRaZxq06/j4+XPy/WvS+RWHNz/k9OOvOD/ck3KiO/8Yt7tiGo/0+5fsX36PXBKqUZzmiVgKXXuNNU8Lg7o+XdCTtWnjgq4+AUl++tT58mdrqfMh3CLKljr+vfRHim/zMXIpzN4zjjOn08SyzMzzTIoJjRTPPhYO84I2Bh2z+CMleRNFI5CcSeTljF8U03QWqXEMOGNExWOkoF8l+k3jahOlCBQCa7MpxP6kpCFSWcb1MrLSlUukREZrDf3Vnn63oekU5milWEge8FJyKwmu/Lb0QYqhjBJdAUMvXJSkNT4UztOEqjwXq6EYxaYfiLmwZE8uIplOOVJUZrOpSjVVKCHjzwFvC3FW4teSMnEu5FhAZ7QRa42SE/MojnA7k/jeteLWKO7iW8Z04M3cMUbHu9PAnDoO8wt87ClLT1A1FX6y5CSWBevtUnHoD9CyP87xJ16g/O7v/i6vX7/m13/91y/fSynxL/7Fv+Dv/b2/xz/5J/8E7z339/cfoCivXr3i448//imvCH/zb/5Nfvu3f/vy+8PhwBdffHH5vdIabSxzmAnzwqgCrW14/gLaxnFzdUVOifeNpDTGnMlKIEoxjiw1DbewUk+kWpVffDh2lBGR1utIBngyDioXoutjYbJem1zntuuzqXWV6a1/rx6lzntVhUmtNQx9T+PcB5+LELbEVCvnTAyeFCt6khO5gHatIBt2IzdzWCp0udC0LX3XM3QdfdtKR50S83TGBy/qoygkNJGeZmYfCCkx+yCfVymYSlLQ9T3n2kGtN6U1BleLlNY6GmNorCFGTesMXeMY+q7KoZvqquswVonZ1ZN7e+W7KBSmiNPiuvLlBHGJBK2YdaFXEDUULcWlrWhJKJCKwmcrRUgqtdBZr/X6gKnaJalKRajG1EWm62skec5rf1AucuWQK2JnHDQdfd+LXDJmctHMc8AUj8ke21QJbtuA0Whbib1PjtXF1lpDzgrnLMU8yhtjjuTiZFNSoBG5M4DJkrqrja5uulZI02Xt4CCVSEyBJS80tKBbUkyQNaZIMqqydfSR69weWZy1tpdOUMy7khQpFU0rpTIOinj9CMlv7VjFSE1MuNSHii1FJWFK52rtQN8L0rf4mbEEbNuw3V2z212z217TdQPWWnKNmc0FtDE0bUvTdDRNjyGiyRgFKfrKfQCLptGGve1RSwYfGYqhLWLIqOp/RQEmkUriPI/EnMVryDpRsCVZNEoUDlgJibiIM+3qOBtCJNaRcwzpg2tdcVZBv1JBZ7Bas+kH0ugIunDwE+F8YHq4l0ZkE3DJo3e3KO0Yrj+ShspoQkmElGnqvfkBOlsuv7usWRfs+AKhfJt+IuuSQp4V+fXjYvnUjEz+Dk+Kk/rC39qvSin4kJgXz/ks42RxCJbncPVjWUKSzwNFiuLpUwygRUZrrKKkQEqFZRyr2OGxLNAVNrZao3i0MrC6kncLYrSn5P5Yn2lV8sWm4vEG5WLrkKuUHCOKNdkHZH1ePwn1k6d92XtWjpKxtegxsk/FGLG2Wh4oTdayH5BFHJJTrqOtUvk74qsi6tBCjplS09ZLypTKdcwX1F6+liKoFaXQ6MJVq+iKoksTU/a4LnEODeSGhxkOc0OMHT73+OJJJaOjknG6Xp/fR5ztJ8/8f+34Ey9Q/sJf+Av8u3/37z743l/+y3+ZX/mVX+Fv/I2/wRdffIFzjn/2z/4Zv/VbvwXA7/3e7/GDH/yA3/iN3/ipr9m27f8wtdgazdC3TMeJcQqkGFBMLMt/YLvZ8N3vfY9t3/Di9oo//MGP+aOvvmFeZ/QVEZHpTF0o8xorLhthrjbvKRWctuy2QzVqLdXMJ+GXUGeoj+9rJY2mC6z7SIwVBEZfCptHlviTf4+ipII1jtuba/r+pzChC/joWcLCOI9M88TiJ5LS6Hbgu9//dbY3H3H1yfeJKfLq6z9iOr7h9OYHbC0MBlqr0SVyOh0JQSLYY4pCvMqZmBIPy8zkA+8PIyEkQgyAQKaNs3RtQ6vAaYgUgXC1pWkcN/srrnZ7nu+vWW3izxSc0Xz20acUFL9mhWHvtBJfihQFsjWG1j4WZilnRu8xRgnnosgcPWSPIqJ0J+6llexagKWIvHYyLVo1aGdJ2XJeBrZqplEBFeXhzFkKEaMEjRD+YiEqMWMrq8JAKWy1eF/qZhOXVI3Q4LgkjjHTbLa421s+/uRTcinc3z8wLxPvXr9nv/UMg6H/+BprYIoJbSzD/ob2nYO7J5dZC1Lktci5d/udmD5pzZJmjsczJSPxDsqiK/KjswZlWdfpEjNFZ3KQxdY2Db1uaPYDSUPSYFqHtpbzaST6iFUSF3DT74g5M58ic14Y88RRv0FrzfPxCxo30LordA5oP9K6lq5t0dSFOkHJSjwXQhS0sBZ7qmhkZXtSoBhNs+0wrSNSaIced3PDF9/5Ba5vnrE5vMU2DZ99+ae4vX7OdrhBmwalLM7KyCCmiCqaxnV03cB+d0VYRqIfSbHgfSIEQQGvtnuGYridNcu7A+PX71Bftig6aWacjNJSjozzPcfxjrvDe0Cx39yy329x1jKdDoRl5jyecMaw3T8jpT0xen785hUhHDkfziwhYpuBrn90SgZoXcvQDWysodOZ+XRPcgZ3tUHvb+nNLzFMGe92fMPvMU5H1P4atjtSOxCUYZpmbNNgnKVvO9oauyGFvBQp5r9rTSGI4UrqXH2bVp+oVSR7IcFeFq2VZ8Hl66UYWf/9Wu18a8NKKfFw/8DpNHI6iXMr6yj2AsoUQozoaua2RoAordHOcPv8lm3foWIS52vvQSu0WWMqFMbqKj02GKNpjMJaRecMsWgiBe8zKhYCqXIRBVU32lwUhloLwfrhPOHLe/7Nv//33N5c8+Vnz5knj+o3kAKEhZLE7VcZg84FSns5f78kzsFjWnlm215QmqzEpqFtu1quZmwDCs0YF1IWaTJa0fYN3s94H/BOGidXIm2jabeWdudwQyNrU8xgInjxdomqUIwVZDNqckjkKWFMpncZM3iShqs8473iOe94c95T4sz75WPeTZ/I+oDFm0IxCVWE57iq2Z/gan/s40+8QNntdvzar/3aB9/bbDY8e/bs8v2/8lf+Cr/927/N7e0t+/2ev/bX/hq/8Ru/8TMRZEHshBtXO0aliDmRU+HheCbnwjiOOGvZbsT1cb/ZkM4TKYWLy+IjZMKlO1CXX4sJVesMXefYb/v68GbZnKqGXYzA8hPEpVSJp1TTKwH222FW8vcfKxPF40O5wqEfBvg9HoVSHVrFZyGmQEwRTI+2HZur5+xuP2Zz/QzvPe7uNcn39G1HpzOdzqiSKCVVEzZ5LeHLKDAOZTtUVFAMxSwi267vSezds+h/qzLIaPHiaJRCG0PfdvRNQ+8cqRRiyfSNGJhtN9J1Dk0rORclMvtZJK0xkrWSn/XkfFPJ8hBUQnIuhaRyNWjLFK0pVpPRhKIJKEIuBCUyUqsasnLE2BIphDKji0blR6t1U5UHhbWjLJfxXr0wUrDUDbYgSpnVzM3nwpIUnW1wTUs/DKRcMOeRPI2M55G+84KAGU02EIOoPTDiLfL0ENukTEZyQmxdIIuW8VZMsmhHbbDrPYMQayym+vAI2VYrLcVVKRfOkDPijZCNEAaL0ngCFOkurdJ0xpFUxhCgBDKBWDwpw7Q8kFLA6A5ThEmiVK7X9HGGX5QiR3HlXe/9nPMHxfqTD1mKMFXJlcbiXEvXDcSY2ESPbVu2myu6doNWwv3gYqpWR62qoLUgHG3bkePCkqTwFpQItDZ0OJoE6uThtFDOCypUWbkWBEobTSyFZRnxYSanSNvKSPJmf0XjDHdxYS6JWSucNXRdT0qWlCxt04h6qpXRQtMPdN9qvmRMJJuhVQWVI+RCTEnQ0G6Lu3pGO3mauzvi2NFcXeGGAdtt0K6lKJHolpBRjZGuu26uXIjegmo9XU3kUy/UalI6cYGG1ytyabweyULrOlYuiNyH36M2Z3I9f1pPXYrYMYiFQV6Fhh+udXWdfPpfLoLQ2qZhs9mwHTp4OJIpl9e4FCfVRNFo8TgyRiIwrBZb+vV9WwWGQoXXL+fydN3OyPoQYqTMC2/evSOkSN9pShjrsycxKJLHlEVNWT5ERnMSF1ZMvc9LufgHPXkKZDxPJfhGoSVcmo711yv6W6EqpZXEfDgtGT9FS6NTBDlx2aGiyMNLVpRFUUjgFzBZHC3rOuO0NGzXm0hUC8/8gaC2nOMoJNp6XgKorajb48jwZz3+/+Ik+3f/7t9Fa81v/dZvfWDU9rMejdVc9YZ5EdhpWibmFJjGmfvTxN3hxMsXz/nl7/8iX3z+KZ998gn/7j/8Pq/fvudhnoQYa8oFslsLE+pXazRdq/nys2fstwMff/ycEDzzMnNaItOS+OHyiqVCt6v7q1ylp06y9SUvHUGd0D2B82Whrg+WVrSdQxt4d/eW8/hEdwoV1Yn4aRTIez4zLhNzmOmGW5rdC3Yvv2Dz7BNOy5Hz6YG7tz/E+DMbBYOGTivGecEHT1gmWSSiJylDsj1u/4Ju/5wynWj9RHr1A5Zp5Hg44xdxS51ThiXQWoc1hraxtM6w3VjaruOjmxu2bc+V6zhHcdz84uVLum7gwr8IiwABAABJREFUk+sbnNbosLDMZ06nO+7PiocpcZwjSykcVB01UwsyEqpoYtEsJQjR02a01WIe5hRl2zBnw11yhBxIPlPqxq/cFlVarNrj08w5WpYSsDnhmDEq0uRFIMry6BBMeSQVCmpTx3qoC3k3J4GdT0lznw0f9Rvc7or9zTOWxZPfvuM0Trx69YquK2w2hcNJzm0MBueg6RMhfriQLTHgM9hWCI6yYRusbVBhgayJMaNUpBm66pNhq8W9FZJsgK7tcbapi2CRwDOtcW1LQvI+qF4MpjMkm7BWFvjGaJw1tK3hFD1jDJzVjM+eu7tXaO1o+wZjHF1jxIWYhC5SBMQkeovF1zk/Yo4VY6hS8g/h+FIyPgVCEbWUtg7bdFg34JrEbqdouo6r3Qu6ZqAkUS2oCvmjqONYMNbRdT03N7eEZWI8T4znmeAX9u2OQbe8jAM8nDn9xx+yLJ5lWTDaMmy3DG2HcS3FGXIIvHn/Y87nOxoNn738hJcffcqL5zdYo/j9/5w4Hu/Iy5m26bjaPxNOWPRczwfpTocOtOXq9mOU2ZKfjnhr8StmgYU+FlIJjIcHmqZBdVv6z3+R5sVn8PIT/DLRbizaGmwzYExL47aM48h8mtnfXtG6Dtt3KOMkc0VVwW8dx6zjSoWojXK6p2RPSZMoWKqPkyyJtQBcN6XLk0kdg6wFKaS08vIU2jh002FKI543T3aunCU8NcRURQlcGoJ1zHMprNZ/U0epm83A/uaKzz77jH3f8vb8BywBWmdEtakNrXO0jaNxFmtUDRHVdI3FKnAKKcJKwpWIK4nsxZcoJdlsxZcly2jFiBPzvESUD/yH3/99+q7jj3605fn1wCcv9rTa0TQDaZpIKRLmI4kO1NXlHEJUTJMSfxsl5GujwTqHSgUfvMiurWYeE9NSOJ7EE8e4hhgL49kTA1jbiTGg1vSDZdhqXKexrcJ01em6KMrWQYEt20pLMIQAh4fEcpxJ+UgsEz5H7pYtS+6www7rCi++eGAfE9efvOK/vop030z8+OELDssNiqbeG4+IyTpG/FmP/58UKP/8n//zD37fdR2/8zu/w+/8zu/8iby+puBUobVyw2ktlVvI0g2cp4X7w4lvXr9hv9myGQZePLvBWUt+9RofA6Yrlxn/NHumSVIilILdpmU39Hz64pbNpudqO5BLR8oD7WnhMHqce49WC6uRzzrrvcCh37pITyWrah0nlFV1gvxbJe/HGCHzPr3SBS65FbHOa3OVeJLBNj3tcEUxDT5l3r1/xXi8Yzq+oy8Rp8QJsWRJQU51nplyJpRCUoqoDco4jGsxJWINtP0AyGKScoZqppSSpNuaUollCFE05oJWmsY1bPotTmmiUmyHLa1zNBR0CqS4iAKpokExyH/pJz48wbZShRDFNMiIuYLWIsnLihQVvhh0quZ8JGRYbSixWj5Xr4cSDFaJMVmvZpwqFFsX7qLQqmBLWVscea1LKVsoWUY7F0izQCiKJStCFvWWqkZ1wi0RzogPhXmus1qlyBiykXC5klfW3+Nrrl9zRRqMEnRCK40xMsZJSUaFSpdLWJ9VFq3EJ8IaQay0qiZz2km/nAunaeTucMS1LaZxmCLGUdY4tFHVc0E+F4uhVYpYCYDWWpSGWGZ8DkxFUVQv3JUiG846404I8bXkREoBH6eL0d0HniCqXlutLsiWUlokjq7B5oy1LUY7FEY+54teQNBUVeFRQSQsxji0Fh7ObneFBva6p0sa9TCR7s74w5msFbptMF2L7VqMsZgapZFzZppGUow0zrHb7nh2/YyhbYG0sjkuKrSixQwwlYS1WpoO51DGMfQNCcv8hBm8+nLobsBahSKLQ/Q0VVWUqWPjTDf0uNbSdOKQG5ICZVHG4toetBXzNtOwBogKgV/Xz2hFOKp+Kk0SjhjuKHmBdBYk5YnMXkZxGnno1GUXKlSH2FrUlyIeHxJ3IaONoragNlwIX0+OtbFTl11tbQcegZ7y5EGodQ9d37Pd7+j7jraxOGvINdZgRTdLTsQAzmjW9F4h96uKsHDJ4ly/VkhC8tNU9Z2uhocrEpVKhqTwywIUHo4FrRPOFW47g63uuEYVDJFQ9GO3hTiYu0b8saSYkstU6TwiUSYLZ26R8VPw8nsjVVW1upeGVl2iADJUVFnrej0uV4nHyqFODpQRLYFpFK7T+IBkT42a4+II540UKDceayLDRnF7XfgsjxzizDkuKDp51smPxed6GX9GGOXnIotHq0KjE5tGk0qDvTcUND5rSk744Jlev+Hd3Xt+5Rd/ge9/97v88i98KQje7/5bztOZ/qZIuuhu4Kuv3/ODH72T1zaKT17seXF7zZ/9le+J6x6Ftmvo+46v3hx4e3fiqx+/YRynOsrJqxydb3EdayWuLl/F4K3afZVCyroSdZPM4JsPnVSfHj4mcvBi1R08KQZyhazb/prdzWcE5VjmhT/4z/+O+fgW9fA1puto91cyy81JgtCCJ1V4dUkQlMYbS7EObRu0QT6f+YqmMYIUFYU6TaQcmVOCHCBqbIi0LrGfPVufQGm6dsPt9ce0uz2u39CkBZLndPcK72dO85nZL5zGM4fTicN4ZlxmglI/YftfEA+EiMhMdadAieIhZsmFWEZIWELRKJvQJmEwqKIIUwEV0W7iHAtpNjgMlsK1OdCaSGvFwrpB4QpAvhDfLr4GtQg15NXIV3wZSmHOmlPWTD7RhyTTiloAi+Nv4DxKCnNWIgHcDi3OQA4TOSngUQW3AnK5ggwxJ4mi1wmtZVadUiAECVqzKEzfYZTF6lYWjqTr5sHFCKttGnyIvDs88ONvXvOffv/32e/3bLZbXj5/wWYYcG6LtaaS3iIxRUw2dMWSs8aUyDJsSCqxcCDEzBQ8e3MrSI6WTTNEiXJwq/NbzoRw5jzfESt6GdOTndpodN+AM48ZQkpLF56hURbXtBjdAEYQJC3FoC72QjRWSkyttJF72VhJff7k5Uuutzu25wwPEw///t+yvHvP9Po95tkV7uU1zfUOt9/JJqMNiUKMkcP9A9EHNt2Gl89e8p3PvmRZTszTSYrEUug2PVo7ki74EljChHWarW6xbV9lyQ4f9QcFSlaaZA3u6pn4Vowd8+nI8e6BFCI+RoKfSNEzDB3WbHBtT4iJ1+/vhZdgHcOwlS67mlNmZcQ9VEmIoOjQhOapCagSSOGOEk6k8b9R8gz5iFIyLlmDNlWxtcBpZDPUVtAOlTFVMmskV4QlJ2IqjHMgm56cbsC+FC+Wb61nUlw/3dHqr9Xq/KwueTXSrikwmt3NNS8//YTddkuvYWgcOgVydtWJFmLw+DnRWFFFSbGqMEaaE6MgZy1NFkLAF+85yWgSBZy7yJ+lGJACpZTC4mdiDixpZpxPPBzv+OXPPmK4vabvt0KIL545We7PXHrNpnVs7QAmo4yoEuW2lXP1vo5CUYznzOQz01TX1I26/D2FcGkew1ADZDAmY3QdEGUuGUSwjq+BIjJg7cD10CfLGGAZIz98p/n6vePr5RrVKr74ovDZbeTXvwx8ZxN58fGBt8uR90uLmW8hN2DmJ2t1HbX+jBXKz0WBolRG6UTXKooxDI1hdoZZy4KulKYoCFkcPXNWfP7xCzZ9zy9+8QVKJ559LFbfSoEKiYf3o3hLNIbvffqS5zdXDK3YXweh8YsaIQozunOW3dBJp2oNu/1eAgD9wjQuLIsgAznn+sxJtbuWyxcVEEpY3KqIBX2t8FX+toOtmBqx6uZXThnC+A7zmdPhLeluT7ENtiQGa2mHDVtnhcwaIinKSCeEQChZ+BquJSiLzxk1Hkgly4JDJgdPyQVnHW2b2GwGzCLR9KtbqZBNs9z0tmF79Zzd1TOurq6lcA8j83QkhJnDdMQHz3mZmJeF0zIxBS+mczkJx+IJcqS0pWkGohKCFyhIGuOsdEGmQEnEKdUnUGM3GpwEiFHTq4tKZDNJR2EzKhVUzizGkE1LSFrWXB+xStHYmmBKhOwFw6mSUlevkdOamCJjypxC5hzEyC4n+ZxLTthqUZ5iYJ4yWmX6ocW2ls0w0DQGqMZeTw5rjZjGFR4X8crfMFrTtx0pGUrJgo4ULYRg43C2R4AORa5eMEpLpoeyDTlmTuPENC+klJkXD5x5g+LQtCze45x0+2I25slJJNnBKLIuNENDUpFplIRmnQLeBbxaqgm6IaZFKq0a7eBDYkwLs47kasFenvB8tDV0N1fYriXHUK3QodlsKG2H9QGjLaoiMqEEdDWiU1kJuqRluJ9Vdf3sNtw8+4i26di7hg6Nf/MN6e090/v3hNMJrTOus3T7DWbbw6Ynto5o4HQ84ifP9f4FWmvhnty+QGnFMs+M44iEBkuicIyBOQaSn0klSKFnO2zToo2YXbE4mB6vdVaGpBxRy3/W9thecf3ys8cOeD6jwoxxCm0U2ThQhWF/g7ENyrVgG9AWZ62sM9qglEEpGb7JsEbuaVVmVFlQ+QSMWK0rEiOIKSpXpFehqdYFuhNERjtKNUnTJaGRzCtRIRpMSsScCEoRq+rw25y6lbdXKuL1P9rOVoRaW0PbNGz3O65uritKF2kbh8qNEP2TvPdQkqRAr59xltT5lDNKa2wdqyllKCVWU9ynCLf64OdfeCnVri4lD8qgcoMPmeMYuT+O9Nbxohenb6W0pB7/xLnIaEmVgvdSW8Q5sSyglGVNiG+iJqvMkOtqXG341/1A1oYobrRZdIbGOIyx8hxk8YpRj7j+igFXZ2klUvut4aZ0NE3h81OD7i1vf2yZUsMP337ENAdMmri9OnNzNXKzS3x0Ncu6nRUXq5mifoJH9Mc9fi4KFFRBm0RnNRbL0FrGOYrJVgGFoZCJOfPq7R1v3x1oncK+uOX7X36P/bbh8+96UgqMp4nxsPDVVwc2W8dmcPzi5x9xu9/Ttk64GVmW3BgKOUiB0jeONPR0vaMber74zhfM88LhcOLd2wfu747MM8SasAxPbvwqS8u1QClKVRKXSOB0fXi+jaSkJNKxWA0EV3dGqzV+OhPv37K0HTQtjoyxlquNdBlWQyyJWDN7YvRCJEUTbSuFSsrk8YCfDsITQYkaI5dqqNay3WwwZkFphfeBGMQYrmTxEtGuZXfzkv31LVf7K/z0QJhHxtN7pmXifjpJUJufWbznvMzMwQtBNuUq6308Z60NTbOhsBDVIpyDpNFOTNW0SZSgiGMU/LdkdG/Qpoaf5RoMSJJroQrKFXQWV9XFyPmr5CgpE8ZJnHCdxRWPLQlHwhAwtka7K5kfa2OIIXGKiWNMHIMSg7sYSEGuuzUyzksxMI2eFMUSW3dSoFgLuYx8gAMjXjLGSrDfypEqiENk0zictaRsamEo97zWDca0WNuJB4IqjzEO2oiqwDoyC8dxZFrE22aZF2KITNOMMYZpnun7jpvrK8hi+CVmWqBbh24Mu21HUpqH8SwFChGvPN4s6CJZLTnNSGGtpKv2kbnMLCZV1UeVda/X2lrazRW2b+Xzq7EKzWaL1ore17FaUUIeVBIZb4rB5kdPGOGWabR2NN2Wrh14+ewl6nCmnCbevrpn/voN0/s7kp/RpuA6S3+1xewG2HbE1pILHA9HvF+4ufqIzW7L7YvnuIqIzkstUDJQtBRwcWEKEypHdMk4Z3DaXQoU23S1Z3/crosyJO0IyhK1w9geaxpuhi0hBHwI4FqUn9BaZOW5KDCwaTZC0NQGZSzUn6G1rWNDGdDJh5wrjhKBGcoI5YQqixCCaWoi9iqmrY0VDkl17qv02lVzuYAuAU2sBYr4bOic8DnWz2Udj5Yn5Uk977KqhJ7cBPInlz+/fCclXNvSDgO7qz1XNzcwH0gp0jUOUzI+BnTMFJXJKdTssTruyOViba+QkdrqWluqAdy6Rq+p34/v8VsYRCmk6AGLaYyMbn3i7nim1ZZr19F2Ygn5bfsA8d5KYGRdjYsYq52DFLkoi3UGazVNyhRdKEakyXN181Z1/yDLOErLpUUVhTUy1jRa9sCSH+MHyvqZ1uwtZ2VfyI0Vo9Od5f3SYAbDf7yzHI4dX795zvsHz/l44Nd+UfHieeB2G5mvZn5450nBVLuJdSb3f4uC8vNRoAx9x/DillQkCPX9s4DTMM0nFl8qEfvRjyEWzVev3zHOI9/9/AX9bouzBlIhe8cnt5/wf/7aFU1XaJzidnctoVzVgMytM30NpEXSWHWmby0vb57R9R3Xuy2jMaTgCdsOlTNzW4jJEGKqXAL5+BVA9ftwxmKMxpoMSm64WCAnIUGuR6ndmY4B5VqUNpgCVskmFvyIf3hF7iy2G3huFI2y9FFDjoTZs4wT0zwxLp4lJSYjyElpeizVZM0IQmC1VMbrA9p3HSkldtsNIQQW75nnhRAiGs3Qdfwff/rP8uVHH/NLn31BQ2Y8vuN0vGOcjtyPR+awcJwnQkzMfpbF13uil1FV8J5oDLY8dj5KK3Qrnia5ZFIJ5KLpnENb6SpSDiyl0DYtQ6vxrgjqVQmsnuodUwJWG2zrwChiLJyLBW/QqaNq+YSMvCzSZbLQqIRVik2/FUVAmOmsyChPER6SZgaCrgmpMXI6nkCBDwsxzKQw4ZkoMUAa0MkyzSNQmKcDp+XDRzNF0fA45yr6Jot4oPIuVME6g1MWpRu0aWjbHUbZ2vFm0LlmRWUZRcXCcho5HE/cPRyZZo9SCh88ec7MfhFYXcE2bhiGAU2GRJWaJ6H/6AJzwJjCzrpqdy1o0skfRCFVlORSKdBFE1Nm9IHSGtwwUFIWW/EnRbg2mmYQW/0QAilGSBnTuGopL4t6yYWssnSi9dkwKtVfG9mA6ubUGoPyCbUEjt+8YX79nuNXrwhv7yBGQSy7nma3pbva44YB1TTEyrfJTuFsx+72mqZrcF1PXEbmaebu4Y7jw3sUhdYajBlI2dFHS/ILKXis0pUL1NT05xYXFRfJWF0QVjpqQVOUqSTagmosxnZ0TUuboxQXJT/62lRVltKGtceXEfKKAqhK6BCHT03CaEFIlcokLYo+ZVeuSeUtrK99aazqRltWPl31iqnxCZcmuojItG02UBwxdxjl/ieb1pM/VT/5N0u9yK5r2T27xg4d2SjCsqDnmV5rlDU0TsaL2miJPHAWp3QdLQZSrpIia2nWaAu9WkcXQeN4qkJ6oh6qthDrMCr4hZwTpjEXJPA0Tbwzmk9uezrlcFrBpUiUw+giERAypSQmUeXZrAmxCGm4jm7mEBjnCNqIBxHCZwlTrpQBeWkBSzSpWNlXrKk5UIrVrnyN9kCt5aKcY6peRQqD0ZlPn08MfeCHD3d8fReZfrAh5YavDs/YfBNp1ISfEls7YeyEsppCW++8/5vwCT8nBYqzlm67ES+KlLnaNIQQ6VvRKHpfpaDKCokSOJxHKAFtF5q246LzSob9Zsd3PtljbMSYzNC0GKXxyYuawIjBj1WginAvrIbiDPvtQNt1NThK44ymdZbYOrRyxAQ+CDyeoq1YSrlwUnpncEZjrMyxD+dFZodxJd4+HrlK4JQT4qPJWR4Ss+BjIMaImY9Yldm2G1qjcUkMgOYQakEQ8CnjMwRrSMaCcUK8VEIqc0ZTPYguD6hzjlIyXSeGXjFKfk8Ikc407IcNv/zl9/js9jnP93v8eGQ83nGejpzOR07LyJIis/c10jzU3CCZ8eYozrWxFAxPChQldumrfDXFVEmhEpyVqk11UBFjLblLZFPEL6WSiEUVkuX6a3m9AiRVyF5TkkEnh0oZnQ0pZ5YUKCWSCbRKrLRpGkzR6DgTM3Q2MyUYk5bIuzozTzEyz7PMrHOqaau5upgu4jBXZNadc+Z0nFlK+IlrrZK68GZ1NWhLKpOQc3ZKy0zdtWhdkRN0bX6LIHWqZmMU2diXEDhPM+M4S2euNama9J3HkUxhv1xhnRMynpIR4kq4y0VGY9TOrVWGoCDWAgUkuFFVgjFKNoyYZPxltPic5CAjrQ8UlkoyPwoSAFfS6k8kXa0EPebLyCA/iY6o36XayV3kjm71OfIBf39gfPuO+f5AOp3RuaCMRrcW07W4vkc3DRhLrqaMykom12a/F5TBauKUmeeZcTwxTmfIwlFolNiDa+XwWQLqdB3CamXRymKMrQnoHyJmTw+xjdcXcqPWoJ2jGtvIeCVEucRaiM3aVDStqmnUWphUYzVpnIVEaXTBFkE8UImi0oUGkp9MYhRrMVKh+/qhropDkBGGQiOKmGpvqCRYMxWHzY1YBHzbUvXxxwiapi6ghayR5XFbX8dcxlna7QbtLEVLnlrx4UJ+Net7QjJzyopeZCEs55KF/K2gZHsp4C5rDSsqzYUDtr7J1TFa0B6xxUcJorl+zHMInJcFXwpJiYy/PLXah0uja0XbzCVjWMlnl3IkFxnphCT8MuPqOAojY96oqDUoq7mnDNiUkCCNqtkFQh+Qa18uzYBGCTIjfHcKVH6kYr+JWJf45PZMLob/+k3i7BuOS8v7Q8c3rmHjElYFtI6gIkWtKsH1c/zZC5WfiwIFpcE0Iosj8eJ2wzAIue5wmvnBD9/ig0i61gdvt+24verZ7hWmWfjBj96jlaNrdnQ6YW1i8QdiXDiNW0pRnMaJojWuHdgOPZv9hufXA84irpLTzOH9W8n0qZbqKUluTA6Jzml024IaqsyvvzzsMgqIdCpgVabpW0LKHM9zdRvUHwYJKoXut1jbsdlcV6viI/58wMQIREzO7FD0wA0RkyPeL8Rl5jyeOfvImBRLuycYQzENMpeWjnwl80rhrS6bEwqsknGLsy1N29N0g6QQa8139jdcdz2/8vJjHIXxzVccxiNvD+85jSKFnvxCSJF5WYgpsswzMUSWZWGaF0FkUqRYx/NcLlFTRXuSfcDPE9M8k4tIYsNiKFlUOsZZ2o8GVFH4kkljIMeMMgFVH3qlDE3aUrIQFHOJ5OJpjL+kqCpjcLtWFvkoAYV+gbEUSiy8ebijpII/39NaxdXWEkxLsC1LEafdw8MBqxXv377FdQ3Fafrdlk+++JyH+9dM0wFfWqaomd7eV1VVbbSeGAfHECgRmrZBKSH3lbUhUkKsPk0LisLtzRXadEKmjfLZlprr7oMUgaGmUfsQeDgcePvuLRfXS60lFLCR8Lx+GBiGQbr2xrLpzEWtdlzEsl0p2ZRKjMzjyJv3dzR9S9t31chJCadBaWypXgxZlAbYIqKDukevd3mIgTd37+hNx+B25BDQKaGTrKQpZ0HFUpZiZS0ijKTRXrgTRXZkFSNqXvCv3zJ/84bjH/w3zj9+Rbl/QM0LyihUa9HXW8x+h9nvUK3k4ajqnXH94ubiBh2TZzwuvHv9FXfvXvP27ddM5wNhPlKKcNpCCIzjWbyFYqTvBhrXoHRCJ8UUT5wXTSnN5Vrrki9ut+qy1chmWK3T6jhYU1Qju7d73PHXjrjo+hnUDDD5cEotFBOQ0DqLUConKEEUPEkSy1elzOWqVDt2hXCBMhKKqa2ogYyijryrP1IRHoRSWiTa2RFyi4nNT3AxLmOciwpLPX73W74g6xru+pbd7RXFwDifaecZtyzkksmpXAJbydWLB/EcyaUQ65ome7aq3C1RTGpk5Fvxq5pdxbeKpuqdq4xs8LmOINcwwiIIpLWKKUrjYoomfqsOXf2iXGPBKqJA68QoKG/TIuTbU2KcIvOSK9Jn6F1D02g2G0FhbFOY58jplLjeIh4obUQ3oF0t/HIh+UiKqRLYdS2SNcoWlM2oNqJSgaTYBEUTC3/6y3e8vD6Ss+fN/RV/+M2npMXxw6+fsemOGBMpS0eTOlHefXBVf/bj56JACSnjR09JgZIjxij61rLfdJQMjTMiwcyPhmdD59huGkqO+EVxOk9YmzGmR6mMs2unmwhxISaYlgllROKYS6CUQOM0m6Fhu2mBQjyOEma3eFkUlBCQrNZiJmeEiIUylCI70Lp4KKWq9bJYW18I7Xw4f6V+uxn2NLnQNAM5BqKfScZcOkyDotWaTikcIhXMVVLsQyRkCMqQjCNZS1EyZjLVLl1kpY8PpfzgWqgYjbUNXb9hs9mz3V2zcZbeWr7cXbNvGq66luwX7mpneZ5HRi9kqjn4+j4kM8h7TwiCqMwhMIcAxqCt/eAN5JKJeRZDupAoWkYrOWdKlsgDpY0EiqVMCgJ/55gxdcyRyeiiIWuKUoJCFLEut0ahrPiDlFKIWkmBqAsJiKVGJeQkHgkxs4wTzim8brFDI4iAShRiJU46vF9QVuO6gabtGLY7xukkmUYYUtEkLyqXkgzlW09mKevWtCrAhPgti6b8IiaxtM7VrGlFtpZlke/ndClQvPeXKPl5nkUmqcDYKs/VYouvtMZVfxuttJiM9W3t5wtLUZQQ5RKpsiqxiTGhQ0a7jDIVd1a1w0v50o3LNRWDw2/zEkqBkBNOJelMcyVbVVSg7tL1L/PY5l4I54+vqHKmxEAaR/zhyPLunnh/IB/OqBCkWFol622Dap2gJ9rw6ISpsM5CfY5CWDifDxwe7ni4f8s0HsXAbR6hZEwdTfml8qlqEF7U4kot8tFCjh9uwOqCGNZNu5oGokrNhXrKgFhvgg/ulm8p8+uHcYEC1sIns5K+c14gz5T63yXzozwuANVXG1XRnvUl1ZodpbkYf4F6JDzr9T5dV66fvnE9daTlcm6Pv3+KbRT1KNEtJRO8x8aASYmsqGF/+Vvr5pqHVgglX2S9zqQqHV7xrYq8PSHyXj5e9VillHW8VR5/v46CxAJC1p5Q/5O148NrXUB4IWvgaEUDUy26tS2VkL7GsjyNI5Bfi8V/wTqFWuT+t0Y4JUqLWejlU1CPJ7O+T6VrEa+qOEOXFS6rjUXharNQSuLT2wesUhxPV/gl4D1MUaOLuMk22hJVRVgvKM3/wxGUH72+5z/+f/8TV9uO7dDwbD/QWMO2bSDC7X7LtEQOY6p5NZEvP9rwycsdX/3XN1JJO4O2gftl5mZoebZpGHYdqJb/+sMHTlPg7D1933Oz60BnxukeZx3XTYOxH7P4xLu7M4sPnMdJGNVZiEMpCqMaND7Kpnw+T3jvGadJNqCcyY3GWQ2N5MVY64hZfESfLt/aWL7zZ36DwXYcvvovLKc7lsM35ORZYkBpR2sd192GTdehi2ToHKaZs/ecfWRyHb5tCK4jaeGmGFXonb1o6lczxVxNtiR7xWCbjqbfcvP8cz66fcnnLz/jZWu5MoZNmlHRc3r3ivF84uu333BcJt7XDXmJYoIlYyFJSp7nRcYNs7h8+lz4pS++5Prmlsk1F0Q4+MDD3T1+KQQPrmtkfpuRdNiupwDLNJOjkJhRGWULppX5vJkNKmuSj9I1ODFEitHTXA9oZ4FA8nA+eXLxJALRF+KiOU8nfBirxX1imj2uaTiqK55vG15cP0P718Rl5s3bVyzziT+1/Cn6YcPV7hkaVbONCsq2aGsrlG5E1phBl8JTL9nVt8E11VfhiU/5CkOnBCHBNEViWAhB7q/T8VTj6yWNOsbENI7kUmiahvN4vhQoKgk6o63henMtQY9tK/k2FIw1bLY7WdwVTOUAy0I2UTJYzAZbDMMihZIPwiOxnSFUD4llWrDGsukHFjLT6YQrGpP1B/wFbTTdZsAVB1lI4TGE2hFTeVysbbYAJmkdB5SLPFaVDMHj7w48/OEfMf3RV0x/+CPKN+9pjmd0hqIVvrWUoaHst5TdFoYBjJjdlVBRGgIlBfx05t3b1/zwj/4r3/z4j3j39husklHdMo+Aom03CKK3qzbloBDZ7bIIZ8d1fbUG/+Big1I13qB26+uoRkk2D1TUnlRrsvxTdv3y+KUgqEWpY+GSUCyk5Z4U7yjLjyHeo8IbVInoYpAqub80VCaviKoBZdEIP0jbhowEh8pnpBDZf0IZadRCmlhSw+wjsThK+dmDZNcC3RpNoyGOJ87LCT1PlJCYjcaHxGHymOrmq4vwZ2IOhFQ4zQuZgnOGFDK9cehO0xqDKQlbEipFiFKMyr2tHwvrOiLVpo4bgTXPZs3FSSESFs04Thy1QjtNKQbKwLpp+yVy8h7dW6w2hKjwAbwPaAtdR/VQcjir8DbjGnkPsQhJNuGx1cZ/O2hUB8/2lputwamEyoW0ZMBgtUE7h9GWsEgOla/j56Z1UMOdizYULIWI0onbqzPbfmawI/fHB757c+K/vdX8t7eKU9kx5YGr/cAm9twtkZjFMRxUvW9/tiLl56JAiVkId2bRFF0YWidQeRCYcuhaUIYleXpXMAWuBs22heP9QkHR3Qz4GDiPZ0zesnVazKqMmH/FUgQCNmIdTMnEHFFJSXdW5OFtW3loQVJJczYss8IrIbqWTLUqrvAjMgcuUN0vRRKZivAKxKJZvF6eXuIC+BzRyeNLwhfZ1AOKbCyt62lcS2MsFvB+YfELY/DMMbJkCAhNTkyzBFr+9n1kdOU1WLmpTdMIE91A27S0RjMYzU7DVkGvCoSF5CWL5DyPjMEzBc8Sgih0wiKjnRhZFuGg+CRE1qzFhXYwlhfX19zsr/nKGPx63pV3UiqvQWvZUI1ZZbhZeEZRwupsI2oVpQrKSHcSfEBlCQ6U8VWRbsVU+3gFscjoYlnGyj1ZyL6QE5dspnXMUVT18Mhis2+bRmLcURJ6FuPj+0NkwjllGufYbHq0TYjjsPRuglp9e1atxcRJPY0+UECpMO0qkSzMs8crKUSC95IK+6Q4SSkzzTOlyL/NKVeXzET0kWKlEBG/B800TZScGduOxhkWHy8Fio+RkAUtEm1UkkCzxpIqSqWcAadlo09ILIEu6MYAIv8UbtOHZoSSpZMwaNxKTIxZ4Pcn3fYlcHPlSqydrJKQN3KmTBPhdGJ6+47l7oF4OGG8l9dSdTYvpCtoHKqxUE3WCo/vL6VA9BOH+3cc799xfHjPMp9JyWMqB6AkmQFp42jagc32WjJctGKejtIkpQxoGtMSzDq8eXy21073g5pjPccVUXl6ruVJMfLBp/iIMqnH4U9FacQsr8RIDp4cPCZFVAlS6ZVMQbNKk5WW4khpK14oqhP7g+ylKCxZvJBKpJSZgqfkmVQgBE1ImRRbsvopBJQn1/J/vpkJSXztzXMMlCgFeCqJkMSCfpoXnBVkwqpVSiyFbayFhNYSUbEWFZQL/gNPn3EEvfrQal+e2ZUbKKDeI4E2p4qwLp6lMSxFPGg+wHSUuigM8+UZEI6ZxKQ8SrJV5dW4Rt7hPEVSKo+gYqk2E0qJMZzKlz0j53riJVFq5pWuBddqNOkXjzYakw0yO9ZyftWM0WgY2kiOE8/37zl7x9lb0jSQgiarUgvUtSL+v1ecwM9JgZKBqOC4LMzBY4DOOZSXB+z25op29mQzctM23LSJz28V2yHw7sdnIpqrj3ccHyb+w+/9gPnTlzTK8sxaut7hRbiA6xqavqHpHSkmljnj5zMxZHKyFAx9v6Gnox9aUZSkwOFBxkGH88yyREKcKCUCAaMLXbv6EohJU1YQciGTsUaTTaExVTVUj1Iyr979CIsmzuIpcsyZaCyp37Dd3XDd79iohMqRt8f3jPPMu/HIkuGcRQaZKeQYWPlUFE3Mkj9ikKDGrhu4vnpB123pN3sKhcPpHU4pdjlyFWee+RObpGgUHO7eMM4j37x7zegX7vzIuMyc5pFxnlj8wjyNgpxMnlQUQTfCgRk6Pr6+4eOrK37tiy/Zb3e8WxJ+XQRyIS4ZshJIsSohumrv7hdfU2AT3aZjs+8w1qK0Yjyc8dPCw/2JUgpd30BSqKjpu5Z26MAYIoUpngmL53y8h5QkrygjDPcSMVphjTzIDulyUk5oY2j7Ae0aijb4WeIP+q6jcQ3BB6Zp4XQcGbqWq50Vo7KYSXXk55pC0faDhUwKnJU8t458BNVytqFrW8Yp4H3m/bt7iXFPErq4LP4nC5Rpurxuzpm2bTmPZ06nE9ZZrLWkJHPqw+FA0zTEEFj8IkVqheuP/oTPnuSS5HeQKBbstsUUR84WvcmUTtCsFBV+zugG7KbB+ABLfixQnpx0yonjPJJ0j3WNZE15TxsT2mTWFPFcNzZd5Bm52Pgjf4cQCO/fMX31Ne//0+/Duwd494BNwqUqTpGMIrca1Vn0tkcNHXSObJXIVGsybRhHzoc7/uj3/yP39+94/frHxBKwjRbjvJrBpKyjHa548dGnfP+X/wyNczhr+OpHf8jh/h2vvnlFSbDvrmtxd/pgTXuE5FdC8uOh1vWf8mgt//TvlA///jr24oLCCklb1ecpx0yYItnLiFwRwSSK0hRizW8y5JKq8ZetJN+MpkNPIiOnKOGvFE/K9+Sy4P1ITIXzaInsiaon2wj2J97k//Ihrgu6+kMVol/IyRNiRmdYyIzTzPu7e7quBXb0reQRFdGhsfhQk9glp0qe7UxJCU3BUjfulIlxtfkX3tPq4C0jErnTZMJZSCFI3ljKBC2KrePpLKhUZ7CmwzZXl3OxjSS6FzIhSVwGCrreEXNkWQJFSa6Y0uAazWZryTnz/v2ZlMTfJyZNCqL+MWR0ifU+0ZCNRF1EMeU0zmKsFT4bivmUCD5wPp4w2tHYDba12NbJWloMKTcUIq0dMcNMxxsa07Nre3h9xY+P8GaKzDlUP6y1nv7ZixP4OSlQ9tuO733xgvE8ssweH6XLtalq3nUgp4hKkavB8dltx6Z1WGXY7jtCURcXVoURePA8oR20ixWGdskkHwnWMM0jpWSy0px9Ypo8cfEoNNdXbeVnwDwHxvOJw3HicFyYZuEtCA4tizlF5H1UqV4poj2wtlawnRELf6vZdB9ertUvIKx5JU0rwVlO0mhTDjzMZ1JYuBtHpuA5xkRUmqDlgW2cQ9cuQ1cER2mFMRbnGvp+IxH2XY9uHKV6JzxrHL2CZ1pxXSJuGQk5kXLk4XDHNE+c55EpesZlZlqE1DrPMnaIWTbh608+wTQ9ze4GcqSEmY83Ay83PUPT/MTtXQrEXKDO4mNKqBBZZqn+fYjCm6jeINbaijgolnlhnmZSTiilyKvtt6pT55JJWT7TDGA13X6DSgUdCyFIsaFzIReD1gVboG8BJZ4iPmbevb/nfD7jvedqGNjudrRdi3NWUJaqvvLLRAwL3dCgTUvqxUgtxjMJLqgR1I0EKFFGJ0rLdhNKwjqLowEt3e7h8EDwkTV9FmSmHaO8/5UkC0LizKXQdh0+BFbVT4qJZZpBgV+8FC1KVAPbjaT2WqMFMVGFtNr/K0lYVU5cew2mmokJUTFTJC3ZWSkKlHzYa5HxVLGF4jLmSFr8InIMwtfKWZANVdU7hYut/aOsuHJX/MTy5g3x7Vv03QHGCZVkA05akQ1ko8jGYF1D0w/YtkU3hmwySUmURIyew/s3TId7pvMRSmZ/tWMKM0tc6OaMATb7Dabt2X38OdfPXtBvZVRmrWZ3/Ry05XwS4vZ2c4UvM6jTT92wP6SWrNdzRQLURfGydt4rsPbIvaiwi8qsxM6CZfVMKnYPjUINCeV2lLSD4kEFef2ixYqsFEoeSXnBkITXoGe0TmJYWWQkLW+vEJUh0+BzFu6FciS1Ad2Ddj914yp/jIJl9f4QFR+swZjKSKSDc5audbRO7lNTbRKMUbhsGPqOAmz6lqFraZ3F1By0CqsiHOxSfYMEsS2rvFgJz8sYfXk/azFZ5z3COYpiqJkrvKG+peIRAYQUiWR1oVWtgaDGNcwego9Cnk8FeQREzSZEXymyvE+CtJvCZufYXVm0U2A1pjGgI6vyLkVPDrHK/xVaiQEmBVKJlJhIZa64KMQga0gIibhk5qmQl0gTA0M+sikNr8JCDA6x8/gp9+/PcPxcFCg3+w27X/yMH/34Na/e3LMshTknOr2qPxLRB4iB223P9z7dk7ImZbi63eBTYfKe5CMWh18y98czPi00jSZlhSoZv0iA3Ol0xFiLdo6jTzycZpaTuFj2w0ZCoqwipYXDwz1390fuHkZSlgq+a52MdUqUVVXXm01lWeNROCt+KK0FCmg0u7754LxNNU9bopd5X9tjFXRaY2bZ/B4O75mnkffziSVlDimB0ULYbBxd117GUqVCygmwtqHvdgzDln7YoK2tKEHEUPikdWwpvAD6HHDTiXmeWPzM+/v3TH7h6CW08TyPTNPEeTyzzBM+eDAO0w68+IVfZXN1y+3HX6LnM/rhDc+t4tZKdsv8rQyiXMTKXlXEx8dEXgJ6nFFaE1LEtQ3DbkPTNDhjZKFJmek0MZ0nck5oY8la8lWUqq6fKVOijFDE+tnRP+vQSaEXxTROlHEiZo3OSbIrlKKzbV2cNLOPfP3qNQ/HI8uy8Onzj7i+uWEYBmzTyPutsO08ncjpyOfPP6PfDhA10S8c70eWBP4J4z+ngsqFHET9IpEeSUIOkzgFow1KW+7u7pnGCecc1lqapqmhfI9k5LVAWbyXxOl+wHsvhk4Vng6LJ6fE6XxCa0OOmZwS22HDpm3p2obUQnGFRBTEQuc6dhMEziqNcqIQ0CpjlMF2Ldo6ktUILUKk0pRC8wFUANia9Gyq5NJ7UeM4J26aSrKfdFGSOozwjJISgy6XZvJ0Yvrxj/Ffv0a/fV/l44XiFMFqKZSMIluHahrazRY3dKjWkE0iqMwUTizTxDevfsxyOnA+HWhay7Nn1zyMJ9Sk6XykTYpnNx/R7PZsvvge/f6KYXeDsQZlNPuYce2O88GTfeRqd8ucjsBrPqxQygcfA3yozC21CKvDCNZk7bU4+ZBuLEhyUTKKzkVVfkmLdg3K7MAO6DhCeKBkD2WuVUem5EBKCzm+o8SMzmc0mWJGCfZjlOcyZ5QbwHQkY8nKshRLyppoOoreoswGVAtF/3eKlMdx1U+MBtT6pRahRZoGqU0kJFIh6cZNsmyGDmctjdU4rS62CSDJ9lorNl3L0Dj6xl1sFNYYgFyo2TuJVZ4tz64ECGotTsWPruAfXrscZeSZspChxZ/mw3NKqRBSEfKYFvuJUmTMa43BNIYleOa5Bhdm8EupTYogWwVLzNKEN7agLFxdD1w/a9GNkv+6BqIGDfM01ZE/qKzonXjT9F1HjJ7Fj+JFRQTVUzBSmKTMPEfCnBiPkMaEmz2bdM++FHKY8KEjC6aMSMx/4hL/sY6fiwLFL5HT3ZmwFKxqOExnFh+ZjBYFTcpYBdeNJvvM+7uZaZF7YiwanzMP5zPTElE6Y12hbRVaCTej7zekXDgcjywh4lOm3wxs9zumcWGcAiHKZuVDpLGafdvhtj3mdk9jNNvecXeaWEJCW0QC6oUYK/HiqwhdNsi4iOpj0zlBSrLCmserrVBsNjsa61ClSMCer54aKZKUJhpFuXmO3UdulCIV2KVEiLFK4BwYCRNr2par62e0Xc/+6pam6ej6HdN0ZpzOnI7vmf3ER65lqzUvjaEtYJIQRY/LxOF0ZJxn3o8n5hh4qKnSh3lkXhbGeRLOS7Pls+//GlcvPub7f/b/pGs7Gh8oJ0OeTzTFE1KUkVCMpGqpDYIkhJjRRlKmvff4ICZxhcIUE8YZDqeJtnV0fcvqUDmeRmII5BghJdKpcjrQGCsks6tnV5KPcbVDIXLDvEBMEIwnaE1MSszzLqSB6mNjxDMlxxlnDY0b+PJ7X/LJxy/Z3dxQ0JzuTwTvOZ/OLPOJGE80r+7ojzPOtFASqRgy+oN7fJomckrkXLDO0g6OXJMSwuI5FfBjwk+RaZoYxwlrPda6Op+WhT/W4iQlSTGOWfxLTHV50lrjF/HIEQTmohMRn5oQ8MvC4JzkfpAk6TaXNWFNusCcyDoSVcBkhU4KkqS/dG2PQuHHhTR7VAzihJkKPFE5KF2zeJQjoZmi5zCesOmWtrSi1hAcpSImsgBrVVBZSI7h4UR898Dhm7eEdw/kECUEUQSkgJEMFl0zfrqBZrdFN+KOOp5OjGnm7ZtvmMYTh/vXBD/hzSxch+OIP42ocUG/WdBe0TfPGDrNzXCN7fdY11O0FouC/hplBz7/rhMHagyHbyVXr0/4uuOVdZxTbdVLKZA9KgdUXqA6hK5cmZVH8chHEduAUv+9wskQQzkuyifVXGTEVG8nssjGVZ7BjCjOFDXXSimDmmvysjgDzwlKGiilJakdWbtHPyDbgOpQZksqDWuS+3pczut/4VhRgBAi07SgjIjE5iLcqNu2wbQNV7UIcdahchFTxOoY3ViJRLBKuHclRRl6KUWIGR8LIUnYac75MeizVG4Fj8+UUJjUpcErKzqFIB0hypqL7n7CSTaEwrQkVJbA0ySWeYKQ5IIumZzBGgsh1NG0q81YYvGF07TQWuic4dm15XZnuP2oY//C0uw0plHgMtopdGNRTUfjHcvDQpwjd2/u5bRMS1INUV9j24BxAZUmSlqYjp7gE+djZFkyx3PCT5lliqj4wEBgZ9+RChxjSy7mscAsTwhif8zj56JAiTExnmdyAKss0Sfm2YvihEKbApvGsO1bcswcToHzVAgRgrX4lHg4TMK9MGCdonErU1vIjKnIjRajZ1pkztY0LYsPzEskRzFzCj6SG01nFbq1lE0PZBqnWLKnzGLk8ygdq5k6AFSTJaXETwBNZ9oKZ4oN8eVQ0HU9fdtDEldZ7yeJdfczPkcCGVVJsl03UFBsUmKaz+jDfb1llChy2p6b25dst1d8/Ol3aNqOrtvw5s3XvH7zNceH14T5TKcUO2u5KgZTCjlFYpasjeP5wGkahQuUouTrxCC8k+BZgkc1G3QzcPv5L/Lyi+/xxff/NC2K9PUPyGEmWicdfsqMy8IpBnLXXrqT9aHXNUg1xngp8lIpnEJEG820eBpnaRuRT6xqjlKKFCjUqIACFIW2Ym61udrTdJpuaFEUcs0oSqZUN1EpTlIlqK/vSSvxDskhknOkdTLj/ejjj/jok48Ztlt8SJR8JITAPE1M04z3E837I/MU6NuhSgYfM3nXw/tA8L5eL4dyoKw4hsYoRWJcwHtBSbz3xKhx7tF9Vs65JlcX4S+kXMeLdf3QSl/QE2NrDlLFrYVTFcV2vuTquhlZSZplxaeLwNZKSc5LqanSOoNBYV1LSRk/LQIz5whBCLSSOK3WN4NqLaVoUoIlBcZlZpcSpmTyU36GohZswszROUFMpNOIfzgx3j2Qj2dUFKMUZbTMA4qpBZxB2RbTdLiuQzvhXMzTidN04P2brxnPB5bpQC6RpD0qZcKSYFxQZ4+6n1Be0bzIdFGxazcoN5B1Q9KarBW23WBdT2cHiJkyzjSH809Z1b5Fia+fKytXJ02oNKHTGUqsHklFxl/I3y2X9nVFIcWvpqiWohxF9VzSu5QC7VBK1iuSGLapEuU6pIQyFoV59ELBC2KjihhQZkUiU4ohmS1FdSwVjUA7Idfqlpx/Onry0z6Bn7atrcVMjIll8Rin0VnhSThTZbcYhk44FkpVg8Q6ixJgTlxmjRKvk1JHfhkptsTHSu7jVUEFj6hOefJclFrYF0r131nf/EqsTsT6nK3E0fVIqeB9Hb5ZIWoXhLirSoGcKFlen4oaOSeE+7YVIUZKEeU0TWPY7x23N5bttaPfG0yvUBaKlnJcF402DdkV0hhJS+J0PJMioBPZGVI70JlI20R0mSB7lsnj58x0zMw+c5pK9YUqkEbakunskS53nGKplJ1vk2T/+EXKz0WBolXBmsxu59hsWuZlojnD21PEKMV223HdWT7eOSKeu/OZrtuw2TS4oSGUTGkajHMM+y2D0wyNZhrFuj1mSe41riGWKFUjo8zpF+luTmfJJynzyH6w6HlDiIlxWuiHjtv9wMnPaKsZF0OgcIFS1sJEKxqjsUbRGIUzK4O+KgzUE2mpUtzcPGM77NBYpvnM4kcSmUCi3V+zsc/pm1a6Yr+IP4v37NIV1zcvLp4hzvU42/Di5ae0bUtKkePhPa9f/ZB5OpP8yMvNFts2fBETfS6kcSLkzJQ9PgXm5LmbjpymkbtxZImR4+wJKXHyi3RRrmPz4jOG24/pnn2C63ZMP/wDYljg6x+S/UKeRkY/MYWZ43RmTJHc7qCKbrfO8EvXgxgkGkUImpQKS4wsSebDIWem48isZOa7Bu3puuJF7+uiI4WfmHtpyaZpHNt5IKcdWimSDyxT5PSwMI9iaa7VY5/knOPlRx+x2e548eIli/dM80K/6en6jl/9tT/D7e0NTT+wxJHzPHM6TzwcT4xHSb99f7egjKHrpEBpGsXmSvHso88BWYy99yzzjHaWbMBEi9WORrtLR5eyuO+ux7Is5Jwvox5rLSlXX5gsPh3OOVlcq0TSLwshBGKSyID1Z9s6o7dGCHYSQKZJORNyvBhLibKhFpFFxjrkpoalSWjgxraEHJjDBFkMvoyzUiQuFUlBvCzOKeJKpklw8hOMZ678gokdUetqkltWhncNrCuU80gZJw5/8EOWN+8I7w6occHVwLyMrhEYhYiiWEU3DJhNT+kdS44sD+95+/YrHh7eMT68woeJzEIokWNe0D5hp8R2LAxzQb8/wZjg+Rk2MxdDVm2qKkNTjEHpgnODoCJDoDs+fLAZazR2/X+xoqqppPpSFlKaUP4NKjxg04P8ec3XUYjfj1GZldGTKvGe1Z24GEm3xl0AlFIt89esnBLr73NEFQ9loegDkmroKTmSvcD+i8/MAU4LeF4T1EhyZ4rpyXYrEtlmh3EtTWt53O8/3LAu29i3lDzlydeCoIbaabKW0E0phuFEolhNSgqTJZct5oxPiWVZCMGjtZgFGu0oWYzTdC74nLC6Iom14vWLZwmiUtMKVE4foDwFUd7oUmiMPFshenLlsCWVUGSWlFhCIiZ1cSC/7F3GYJzD50z2YlooY/aKSqrMPBemSTydSoGH+wWlNX6GkgqDNTRa3lGJEyWCsU4UjOvMPicudn8ajFNsbgaavuFwd+b8sPDqR3ecQuF9uIJhhxo2fP7Rhl0/o907Ylo4xTPnJXF3lKlBjIpYOgIdhoxTwn1b7+QLH+pnHPX8XBQoq7mMQqC2tjGEaFFnmYE1jaHvDLuNZS6ZpRT6wdE4RzNYYslMyeHajqtne2zJ2CJmazFIvHq62HZqcqKSEDMpW1Qx+BDwPmFChmg4bsSaeJyDQPKtmF05Y1dCuLyekvevtK6hdBpnJBnXKHXxeUg8GSnIWV9cAF3TkHLENYI0FApN2+FcQ1MVF6mIbbs11ZABJRuyNtLhKiO8jFKY5xHvZ87nA5SM04Zd29O5huF8psmRGAQ5mXNkyYElBaaa3DoHzxwiS/ACk6aCahymG+j2z9jcfkTT9miliA/vKMuIOr4nx0SOkSXM4pcSPD59uCh0RvOib+pnVghKE1NhIjOpwn2Q1WEO8ULYy9lWGW7dkJbAukqu5kfaaJQVSaCdLd6LeiH5JKhE8MQsAwXZoCXjpO06nj1/ztXVNZ988mkdZc0Mm4Fu6Lh9dstutyNVgYoPseYWzULongO+BEDjlyy5IQ60Wz64x1ejtdVoKpcsgL2Rm2ld9OU2lY44JUlcXuXCjwZSj4nGazx7ilG6vVUiKVXGpQM01e9BVdm50ivBuL4/9ThikNyydSQh2S4rkbEoxDW2yKxdGi0lbrNFy9x7PedSxKW2KHQWmDzEQEyxSkoLK/ETROK9kmfztFDOE8v7B+a7A3n26JgqgiDkXAF8ysX+X1kjG5yGUOQ6LdPIMp7Ifoa4UEwgEVmix4RM8QmiwkRQS4A5UUKgxMTjTvwoC6aerxTMVSbvqhnhhXvxyLOoEMUFJhe3X4lIKHmi5KkWHutSXiXCRGloZDeS5x1xClZI2rVYpa+ojBQkpSJiZPm9KgmKh+IpzBTCRW0lxOtcnw+IQRHweCYyMyVLLosuEt2hsyihSlFPipR1NeOD0cnjTVA/gvX3WshnupoIirGhnFcg4TEIX3hVvD3eI7l+Ck8WYHkeZIK+MnUutvryvKQLQphXYvb6pp7M0VZH8FXkcBn3KC5NQaqBsB9s1qs44vLM1WdJrT8fQXSijKlVgeBFZJGi2FbYOmISXl6WWBIL2qz3Tx1Hre+27jmmEdK7axu0jSxT4DwH7s4BP2pi29E0sKSeoQRynDjHwhgiZ2+JSZGSItKRSkspriKg37p86v/hIx7pNSz3x4nTecGS2XWO/VbIqMMQuLmxfOeLDbff+VPcfPqS8f0dfppY/JmYMx9/3tH0G65ffMR8HJmOJ3KIRJ+4e/+OJRaWKEmTSrWSAOxPKNOilOF8ODPOkdkUlsVxtXVMIXF/9tyNnk1j0K7Hqk74IjGjrRNFRxR/FW0M+13L0FhSlPyd14dR/DhSpH2+8Gk951IyX3/zAw7Djv32lqvrG168+EgekJy5v3/L8fTAq1c/YllGtNIMw47vfvdX2Wz2XF89I+VASp5vvvkRx+MDf/Bf/q1Y4ceFthUuymfPPuazZx+zPZ9x88zof4QPRx6mMyEF5iSjpFASp5A4x8Q5BPE88QupKLJp2b/4kpe/8Gt89J3vc/PiM/rxLfbhNaev/zPaz5ggY6I5Rc5+Zgyewzgyl0K6Tpc79Vnv+H9/fiPx7TkxLRkfMudFM4ZIawzvZs9/rqRZrbUYaeYi0sKcKJfQxRq4haLtevqhQ2tDSoVpErlh9ooYNeiGza7D3WieP3/GZujpup79fs+v/x//H3b7PTe3t5fiQ6R8ht22Ryk4HEZ8TBLMd3/HmzevUPEMecE1bfU4WCgJzlNk2M6X+1sBTmuytjhtMFrIAsVC6CpsW7SYrKFoh54QM4fjmZQDthoBirOpx/uZAjQq09QgsTmsvhdceAUiv5TCKBcp8lxjaYdWkBdlGDY9zjke2kxUkZKoEnWNNrJg5uhJQZG0Q5cEp4danDicNrS6FROwDIvK5OpUmkthir5KKBNd42mcZzlPWNMQ6vu0xooDbB05EiPLV98Q3t5x/5//G/H+iDkvNabBECuisCqHki4onYgugwq8Xx7IWs6HsOBSpFeaaAwPacQnz2Eaab1m5w3xHPHnhF0SOmZCyQQiHo8pCzoulyu5bnK5FnxN05BW57WfsrLJCMrWYlo4IwpF0YFiHFMeKCVQSJA9ZXlPDiNpuau8F4Pqr9Guo+l3KG0xNUNB1yJSk4BFCp2aC6Wr3w7It2LKxCWTYqEk8ZlaZlF7zYvwppSxWLVF6R10L1B2Q2n2oC3aiseQTwshR0I2F+XRTzvWIuUpN0UpRdf3uL6lv9ljmwa0kFFzjszVYC5UfpWyVnh2StFsNqAK0cs4LAQqn012/SUDTtUiXBLhO6OIWolSKBWKL1LYXkix0rRKgfE4ri8KQbxrJeK9ZzKaOUS0SmK0Vs8z50xMazOlhWRuNE3j8CEznTxRYpZwThqCuZJlwxxlBbNiWte1mptnHS8+svTbjqZpKLUILUnem9AJZORnJCeD6+d7tG5oNyM2Rcg/5s3rhtfTnj/60Wc0Tcvzq+dYPZPzkWXOHI9CFE5ZEbCk4jjM32FJGwoNF0Laz1iYrMfPRYESY2aaPNO4ME0zXSOdgzEaozPOFvrecH3bc3O75eZ2TzofSXPtFHJGa1HIeC924CFGoIhJmtZoU8SUjbVwlrm9qdIxXWeOOYvcLmbwsTDHQuMzphQ6I91tjon81Ib5UtGvFt2FVP1IlpCF41HSE64KMqqIkRQDzgnkLv8+k1KslX9+NFkzDldTj0sp+OhJcSZGT85iVd42TZXNtbimo2sHWutoUTRZ4TIcU8bHxBSDzHWVqAKSEkO79b+VAV+0kHC7zRX7208YNte0bYc6BMo8kuaR5BdJ7syJOQUmP4s82c8smQ9CEq3R7HtHSBqfDUYlvEmAQWvYRZhqrH2p3dI6k1eldkd14RWZnqmGWg1NKxuvcw273RXOOIhVfpwSjTM4Z3j5/BmbjaT7bnc7Xnz0gmGzYbPd04ZA1/eCyGghNostfiQsnjCNxGmiLAuKhFGFrnWy8XcCE49nSdF+elyMwrJ0tVkVlIZi115bg88Uo0Ri7Vzl64itva7IhxjHSaBazlVevsIgVWKOUk/4Cyvl7/FWVSshUCFITk6QE0pnmbTU97PGNoRYzau0XIMcMgqD0xZbkph/1e7w6XpWqP+uQCaTSiRFT/ALfpkJVbGDE5hdaUVZPGVemB+O+LsDcZzJi8fUeygp8WQWRHINFdQo+QmE5AnnI0klIpF5mSRHJwXh32QJLRQyu0ZpK4X6tNCnjKXyCLS8ooSJxot8tZRUu3kNRZOz+NB8cOKPe1uNMxC0SUY29TlXG4rWZOMoKlDKTE4jyxQJ08R8uKNoQ9aG7mbA9S24Fq0bonLyeeeCronG2QdyLMynIzl6VJHcKq0SOQdyWshxIaeI1eLblGO5oAJFaZSyGNOidA92ADOQdSd+KhXZLSULKTk/Wf/+O8fT0UApBcnnbHCtKA9VNcTMlItBWkrVeI1yQROddRgrqJWffSWtenERqsiCCJbW8ad8b2hlvVTBE0thzplYKs+kIl6rA3hai8+ygl3lsk6nlEkxiUGnyT9xjrkUUhbCNwoxWbMNOkEp+oJKmgtq8+R2UUIxMLrgbGHYOLZXYmkgPCuEkKwe34+Mt6v9fZEReNNa+k1Ds2Q0CyVHYsg8jA619MSyx5oOpVoh9vpHiXRUioxhLj2RlsfktKf8k5/t+LkoUM7nmR999Y5xWlh8YL/f4JwVLocttC5ye9Pwi7/0Md31Nc224+tl5HD/nuPpSMiK2e2JZPw3HhVmVJzJPuJcYddd0yZNvJsJJRLLJFkHuqHvxQZ8aBdCAKsURjtidvhUOHmB4IySTV5TCNMsVb4RaNdUckRMmXH2xGSIQRNiZpwDRWWMeQSznx5KazbbgRgjb199wzyPjOOpLsGFm+cfobXFmAatxCfkfH7FD7/6fUKYiXGm6waapuX7v/SrtF3PdndFCJHzcWTnA+rhnnI8k+aZ0+nEaRp5mCa0MXTb3SXNNnESs6GipUhLBW0t/c0Lbj7+ks9+4ddk/hoj8fAeDm9Q00wOnvPs8TEwhoVzXBjDwsM0EVBsqtIEwFnDfjMQSsKXRLtIMelsoouJqWnInWUfIyEk/BIvBE5XPQOMqZ2PcdjW0fY93aaj7Vv6zYZhs+HX/uz/i81mS98MYsrmhBvkjOL6+pq+69hstzRtx9XtMyn8lMLlhj6Ln4CiEMLCEhPHw5Hj3XvOr78h3r2jGc/YFkyj+eSjPZv9hpublhwT798fcMNwucYFkVPPPlLihLGOXjsam3Gt8GeMMRQfyCGz2Q2oDN/oQvQBv8wsy8I8T0zTiPcLrtrl6woPr4681jm09/XnykK9OvWua00uuXqwFM7ziGci5hksNMNw2XhbLdkcxzARlngxGlRZ0WhL32zJYSHNowTfrie7/rIUQhJvB6wmpIlpURwe3osSSTmUMbRDj3OWPraEuwPh/sjdf/kR8+u3cDyjF4+qiEkgkiTijpAl0bZYUezZMhOnwP2P7llyYEoLJgdUifjlgZQ8gYUM9KrDNQ7TdIxfHzm8e89HUeNMgxoa6J04puYoG3t+9KRBgdKSrhuAFAMf7NWqFjha4P4EFemrHhOlA7MVWpYTczXiW7x/w+vX/4HD21e8+cHvkbQl64ZPv3/F9nbPjbrC2J5MWxGZglURqyLj3cR08vzhf/p9puM9eX5Aq0xjipizmYSzEkp3vb+iaVs2w+aCUqItxbQ07RXavSC5F2Q94EsjDVdeKERyEaS0ZhF+uJY92csuCEpt2EAI3Ff7Pc3Q0+62hJzwIVSb/YxKMiAMMUiac8k0rqHfb+mHlrZtOJ/EK+s8vhEUuxapqRR8FCn7EoTX8vnL5xRgnGfO88I3DwdOtXkVXxMj8nqliYifUKFUlZOqBbbQARatmecFqxrouBQauci6Py0iN8420bSWq9s9OUvTlYuvqruLwFpkyLWJURQal9n28OLjgU+/u6fdKrQT4rh8tvVfZiBCiUXSxgs41zJsGj764pqoTvz4m/e0eqY1gVfBMC49Pz63cg0qqRolcSAFD8YLwVZtBbH7VqNxIcz+DGjKz0WBsnZztnWoGi4nTqyySbeNQSvJyPAPdzB5zqcTi/f4AEssvDnMLFExeoUtHodnaC2ta/j40y8pukXpV4ynM3FacNrSOYut0jNZ5DWqSCifoBgZU7+vK4lPkekag45iny+Nlarwm2REWK9QNFCgbaXQ6ntN3z36oBQKp/OBFD1fOZklTuMZZx03ty+EGGks2ohpzjzPhOA5HN8xzyIb7vuBvut5/vwTNsOO3f5KNrqSyWHBj2L4FX1kOR9gmhjHA9My4lPEGUkzLikRS0CrCnuWdWGRjSAqxXE68fU3f4QKCyotXD28ovEnbGW4z9Ezx8DZLzVQcGFaPEFp+sKTmhxK1dcbpYVIjBUjMa0xjXQS3ehgUsSQ6+deu1YUVot0uxSFUUaohClSgmK3/Zjrm1s++fRztpstVllyjsQ001hD6zTD0NM2rTD9p5nw9i26kkdt3cxN5WekyvlYFs80zRwPBzGLS4USC1krmkbI3dc3O1KKhBTBPkYZK6VwXUNSGTM4TGehMWSjSbmAEZltNIKoNI0lNvJekgqEGCsJNjLPgghorS+W3Gu2iaBtQiJknbknSeQ2STr9UolQJa/oRhblRpZRk6VBawPKYdHYpLEpCc9HWbGt1w6DQVW599qFUjvay3nXEUhrDRvbYIsil8j5eMDPEUyHdtVzwTmaqPAPI/PbA8vDiD/O2JApCULdiGJdL7JG1FmloJxDN5ZUPVFSSszBc1zOWJPRFXbHWFSKGKDBSlp2SqCF5IuR8dj636VprV3yyiFTT+7nktYN7el6JjyL1SMJLdEK1S3moiqRzr9ySFKkpEDyCzkK9D/0e9xww/7qYzb7j7FuizItFEHXdAaFr7wMLVtqDKTgCbMQPIMqOCeIXmMbrDHkbAi+cAgjBUE+s3Zk0+E2HbZTqK4Bu6G4XUVX6sll9URK/tM3rFIRitUg7ema54MHb9AhEGtq8EqqVUWkuSlUlZqq6LZW9RlcLhymx1iFcuGnULLwC2MkpkxXxJ3a9R2NtRQU3eKx88ycM6EIVJyFVFUt5+W9q/U5UdIclVyVQWn9uXJe2iic09gMRUOsWUan88yyFKY5iA2FFt2Uqij9GnWhFFhdaBwMXcFaIVOj7BMkVNBWUSsqyAaKIpXKr1HinTLsHJu9Y781tLNH5QOUE4WGrDvAUkpTUemn4Qz/F3t/Emvblt71gr9RzWoVuzjlLaOww9iGZ0OmEQKTUvKE5I4lyJRASDTcAgkaCNwAjEAIC5miZdExMg0L2iAQHaOUUIqUMkkMmeCHMc9hIsIRcYtzzzm7WsWsRpWNb8y197kRYezAiJfxPKV9ir3XXmuWY3zj//0LiX6R8Nui/HvAz1mI2N/O9h1RoKAUOEPb1Bhj6MdJLL1LL3vVGrQK7G6vGK7v6KNmf3PHPE7MXnMcM1/9cM9xSNzuA62NrFzk3efn1I8qvvA7fpC6O0PxH7l+/YrD7TWNdaxqQ/Ce4L08CEZjgocEfp7ISYyBrLFYZ1iyMDadwwfN/jgSlfgx+JSZQ6AfZ1JKrJoVlbVsu4autVyeNZyt29Mh55y5vnmFIvPq6kOcday6Dc+fvc+7730Xm/U5bdMRvMf7iReffI3b3RVXrz/gcLzj9vY1n3n/Czx98jaf/cz3cnn5GMh4P/LyxVeZjrccbj6mCpnWZ+bbG0J/5G7/SvJ8kkfTULsVKE/ICm2sTE4gKzQlRh2zzry+fcnuf/3/EA/XMBz4HeuKc2eotSKkyCFMDPNU7PBHhmli7wPBOM4fsINlsE8nYqW2YHViqGp8jpgO3KBYjzUZmIZQCK2qkMM0VbUWJeUoK2sTM0wzMXkenV/w9ruf4bu/8L2sVmviNNH3e25vX9FUlrZ2YrpmHVfXt0yzpx8GrHN03YrVqmO16kQKqLW4L4YggWG7A1evX9PvD4SYyV5Kprqu2G46nj6/KATWyBgb+gdGbc2mQa8trDWqMtA5ktNMMUp/XyWxZa80dePIU0VlLV4pkR2XIm2aJmIMOOeK/X2Qc2ktzompm1a6uGAGkZH7IMcSIrEQhVNKxBDEiEoliBplLDUtRjmsbiSyPSSaCDpZUAajDJWtxSht8hBFoRJTIOXwBuyvtabpWtaN46xr8AeP7z2311eQ7jDVBlc1qKzJrqJyieGTOw4fvmZ4ucPf9eAjOpW2Ts7MpFOF4LOYvKnGYVY1oXZ4CyEH+nni+rDDNRpbadatwWqNncR9s6VwBOJMYzRVU6N7L+iRFgRELQTFJO6/sRQiS8NMLy2C+I1QQlYKTAadyDqUqAUhRkuRgpBBcyAnTw4jaR6Icw/R44zj8vItLp59jst3vpd68wSfHTkrIvaU36KTkVwqraVdnWaIM2EuE2GCuhY1obFb2rYh58A0BQ77W7wXG4GsDdk42vOZZr2j2k6Yeku9fYa2DVqLzUHOWqII/isTVk6JVPhjS5GSc+bY96K6bKyQnbW8V1nXF+v7mZAdVW1O6kg/TwzRF5PCWMjG4seelbg0xyTZRNM8E2JikyLOGdbdipThcrPl9aGn2+95fTiwn2dJNl9ansuUXXhCqbTkc5Rr7H3Au/jGcVprqBpHoxUmwhgFoby6PjDPib6XXCxjTNlHScReBtklgLurM+frjDUzMffk3J3ageWmE78YZeTu04Dy4q1EQrnM9lHDMHoeP674aD+i4mvgkZRT9rm06nJTKo4AWSwAoCqnciHIeu6LsPzg6ze/fUcUKCnDHBI+SoJmjBKCF0Ni1pHdYeTxuaVqa9r2jMfNmpeu4nB3xF9NaB9waqYy0NUapyVJdZwyu53nk0/uaDegnKXdrHn+7ttkP5H9QMyQk6JbOZKJxD5ircLUhiYbVGWpVYAUixUx1FWDtaL88UkxJjEHGqZI7TSVM3RdReUcbVdjNBzHkXW4VzgoFOvVBSpn7navCC5SNx2Tn+mH40mBMxwPeD8zTj0xBh4/fsbjx0/hs9/DqtvQtmtev/qEm+srDvsrZj/SH2+LX4DCW8tkLeNxx2zg6DRDhsMk5kZzmAjBk+Iscm+rC5tc020f4bYXXHz+B/AxMc6BmaIuQchlsUC+PosccPCBwUf6kJizOj38n7rigDkZJykgVIaoFLbWVBGqusK6SC6sfwqKpZWiVsJ0r2tFpRWNAdM22Lbh8++8z7P3Psd2cwYKPn75khcvPuKLv/LLtI2jrSsePXpMXTV8/OIlwyAGdcYYmrbl4uKcy8tL3nrrOduzLc5VovDyErkec8anxFiUB1opxilw7D031zsgl1Xhm0fcXnTULjGrUFZFSohvcyBpCUQMJdxMK9BWs96sSClxd7cTP4cUC58iiHdKENM2rTXWWZqm4fz8nP5w5KgUcylCUvGQCIUsqBBL/qg8VddQuYbuXIq22nUCa/vEfByYeiFoOww+ivRyzqN4lBwHlBKEImRPImKyK+0MGd40EZIq3BOPnwK7V3vmPjONmqpqeOvtnnXTELo1xxev6V/dMu0HYu8Z+xFSImbhS0WrMEY4CalyZKdx2zVp29BXmllFDvPMrCTMMGYJ0jvmIBZaYSQlGL1hfxi5utlzdjWyvp1YzQ2tMycOyWmIzvdfZQYjk0rulrSZPn17i+JXnHtVlEwYYhB795hLNlDG5CQy4/kI844wvyYzYbo19fY53cXnwG4I2QraldVJNSZVWk+ernn18kvsrz8gpUjbbXn2zmdR2hLRGBOwJtA1gcomgh8gRlx1KSjMvPBLIClDPySG+UO0fcGZv8XVa9rt+0RdE1VDIt6jDW9s92ynjBQpqsQroKTB7b1HWfH+SepeLaaQEFgD+H7E1omm7ojBMxwPhChOq9MsAoWc4omTJqe8oIFZCn5nJPVXGUMMQVzCleFJ17Juarq24Xac+Hh3YAqxtCgVonxaOFUPpDxZnZCPe/xELO2N1eQ5FFt9RcyFo4NC6YSxksHjQySnTF2k2tMs5apSmaYynK8dbWMkJqUUTAt0pZazq0FZJYaG2qKSIQaR5qMyq63l2Tst57ee7tUNZvc11HyHWWfQm1JmKJRKJ4R8OT5dAIE360914l/977bFk7L073IWMprWlowu8eyZvp/wIeLqis3Flu78cWFAW27vbjE6Y7Wisoq20WjEgXb2cDhGrq73dNEIn2Ld8Vg9Zdjfcbj18kRoTdM5sk0MYY+14GqDo6KiBt+jgvQulYJutSJjGCdD9hki+JiZfKKtDXVlaBuRCNdNRUqBYRyYHxQoKFh1W1TOvHr9kRC8UmReCpTDDcOw57C7JXpP5Rrquuby8RPabs3m7EIQpNlzc33F2B958cmv4f0IJLpuxaPL5wRbMZuKozUMRjFYxZg0xzGhUxCybZQCRakSaqeKBHd9RnfxlOfvfDf7w47Xr18QUTI4qKKoSJ6Qpc3jU2IKkTFGxpCYtUDPb97WMsorRYGOxQApOi0FijO4KlM5h7GzpNRqDdqgjcEqRUWm1nBmNY2GtYVq3VCtt7zz9DlPn79D13UM48jV6yu+9tWv8e///S/StTVdU/H82Vs0bcfXv/4Bx2PP3d0tSkuS9ZOnT3j+/DkLgW692RJiktTfIETnkBNzktWbjjBOkWHw3N0dJNk0n9Zip2tdb2voFHnsT9LomDLJRzAGZcXzIZGolPBSupXY14MM9hIUGE/FyfLvxQ6/qmo2xlHXFVpxkiQvraDl3zLYJoL3ONtiW8fl5gnWOFTSzHMghJ4wRobdQNd2OGeZs9jhhxCIfmbqd8ITcxqPFCg6mwcFSsaoDDkSvCd6T5g9d1e3HG4ndlcTlWuwo2JardBnE/3VLePNjnicyKMnHGdSEL4SRpNrQTMdGV0blHWYVQvrmsEpppTpo8erhLKGGCaSn8h+RBHQcSJFGEfF7mbPJx+9Iu4juc94e0m2zT0hkQeFSQmkWy5oLh4XSfMNapb715evmFCF9H5KyS3ybQOo5MH3MB8I/paMxrYd9foRzdnbYDpiMkQ8MSd8BjBoFNEPxOGWm6uvc/vy17Cppu3OeOfzvxPtWoJxkEZU7lHxGtKBNFpSzNhug84WG6XNm6PncLhjGg6E6SUKT6OOsDqnbS+kFanrQkz+FDGYe62AIE8le4p7knbOIgzQ5d5NiAxd5tt8CjgN40wAVO5IITD0gZSkMJ9GL+3VWHgB5X1jWRjElIp9vZF4Al1k+irTaEPbNlw6yZKqx5GrYWSK6b7i4GFBsNwKxQe4tPreOGYtEQ25FEgpGVIuCroshpTGKqyDUDonldXkrJg8iPmeWGtsV5a6erNAUUVCfz92ItYEpUDJqWRZJUk/bteWx89atl8/0FYDxmvUsMdVG7CeSAPKlAwgh1IWiU24l7Gn08Xk1H67Pz+/ue07okCJKTOGdOqRu2JjLMxvTdetqZoV2dZc74683M3s7g6MY4ASLPfovMFUltX56gTHjoeBFOHsomZ9vmK1eszxsOPrX7olFF6Bdh1d3fD+02dENDcvatqm4ru+53McDj0vX94y7W+Y+r2cbQVzlv5y3TYEFQj9iDOGddvy1rMLtusWjVTRt3d7YvR4P3I53RcoWmvee/dzNHWLqxz9cOR2f42fP2Z3d0tOHnLkyZN3WK22vPXsfWHAO8vCpp+HgXnYcXP9IYf9Hcf9LdY53n3381w+espnP/c9JO/J3jMbmG5qgvL4ocdnxWAsr7UvSgAIWgL4kuvIRqHbDWNIfOmLv8h43HO8fc3TtuXy8gmrVpChfhiZYmYuXz6Bz1qkoNqizJLrIJvSCussyTqidcSpZETomTEn4kGhfWbV1kxrz3p26GTQSbGOwk973lWsnOFpJ5lJtdXop++jL95C2ZbbuyMv/l//b3a7O375P/4vvHz5Cdevr3kdpeXx9a99VJCRWVyMewmPzDnz4uMX/OoXf5XXr1/z7rvv8gM/+HtwVcWLj1/w8YsXfPz6FWN/YJykeNJG86WvvuT17Z7vyZc0raPp6oeO75BFzplnId1mwBSkKpPleuyPoipBgtm0VZxvzkg+0TQN4zgSghh1qdL26fue3W5HjJG6KJiaruJsu2U89PSHHdNJYSKkhxgj4zgJNBwTW52px5qb4TWA/Ky04awxnG23Zb+gooSx5ZliGytmhyExx5GYA65tWLqEQo3IoqyZj6hsoTa4dYULiZsvf0CcEoerKy62Z+yfvwVzgDkS+iNxnJknCYf0SlyVtdVUVaJqEvUqY7rEmG/Jo8LfJGLyTNPA0B84Hu9QeUYRcF0hSetECJlhCFQ5cLlu2abAKidsUmJSdWobLG6g9+jJwnrQ5a42MaPTpyYtCiF+UYQmCXQjyGlLUXyNIhGdAzpMhOM1U3+D9yO6OqPbvI3uLghaSMs5Fq5FIQsv75/8RBh3+PlACANd95SqfUSya5Kt8aVFYpXC0mPyTEiaHBNarVGqIatzyUHKYFcHVvOR4+svEqdb/PEa4szYviJVHl9XTDkxLeqvh7f5MqtzAprKeSt8EvJpwnOuwpfnUWVwCtbG0ShFmj2ezDQM4iZeWWlPRgnBy0lakhQvnqwW3qAslLqmxbkKV1Vi3OYLOhQCVoFTikdtQ9vUfHRzS8qJQ/AF6CnGBQXZVUr2rdLy9wOfZABmH+lnLy7lKXE8SmCpthplNFWF+G3FuSwUBGUBMfM0SuGUtLZiSV1WdsksUIjRp5zMpT2+kJ6UFi6KUU64NCFQtYb1heWddxyHPrC+0Oz6zMe71xznntfDhNI1Vb0C1aJ0S851KVLKTf6A7Syhit9+pvF3RIEiQW+iQydLwmzJdpLAvabGVjUxa/px5jiNjP2En8Q8BwVdY2lWNY+erglJ4ZNiZyXnp2oMdWPo2pYwj1K9l9A34wza1XTbM9AVsb9htWp48vxdqtsdw5gl+ppMVnLjKFUkwCT0nE6FVO0Mq7Zjs+oIPol76OQJweOjTMTLplCs1xvW3RmHR8+wu1tud7dM08g89midMVrhqprV+oyLR09w1okSwc9M4yCwuR8J80DwI1oJEW6zveTs/BHnF48Zh56hP0LTkJsW1XRoFC5ptNGEusJEMatKJYAvGUdUEp6Xgme8+oQ4HPGHO6q24axpccYAiZAlnTgW+V5MWaTKFL+Bb6i+5f+Scqsl5C1D0sXIbAKSwllN3VjqlUV7jQmKtc+0KC4ry7q2XK4a4acYRW5X0G2YQ2TaH3jx8de5u73hxUcfcXt3y1xUMNM4Mgwj1gopNudceB2CRhwOR5SG7XYLwGc/9100Tcd+v2d/2HPsJeXYFyRCZbi56/Ex8NbTjpwztq4EMn7ADBbCqhhHSVtenCETmTCJFbWtKskDykJWdtaW0ElzP1CUcxqjJBoPw4Atx6EQLkrlKgmQ1LKSFe5OsfPOlMBBTwiBum9QWREnIXIe+6N4uljDarWmatuTpNRkyqo33vfnC6E8BnFszs3Dybq0MqIn+BFHizEa2zrcGAnJy/2+PxDniVVd4ZTBYvDRE6JnztJC8SajTcZWilxlcpXBRayFkDzRJ3zywn0IM2HqCf0RTcCoCNainGQ2pZRQMeA0rJqKdtLUc8TMCophnYAA5ViK9rRwW99AWB62Gd7YSptgcTtmQVUyJdm6mPXlAHHCTwf83MuYpB1Vu0W7lqRsmeRTkYVD1vc9pxwj0c+k6EkpYFyDqVZk7ZAMJIpCzaKVKf42SsIZjSWpCkxLcb6R8+8cft/i45Ho9yhlCPORpGqiFbXLQm5945DvQY37CuWEshSyJ4CS3CuVpcAxSiay2hhqFOSZnCTSQRxgdWmXlSTxdH/80oIoLbcyiS8OywsanIukOBXbaR0jTXHo7mrH4Gf66AtBtuT13O+ypCgjnKNPpy7Fki2WCuLqveye0apYXCgg3cuyS6aSQhKarZYFljXLTbMUBTwgX6uyW0qK5QWFRtqRi03/0m6qasN2a3jyxIAzbHvNbvL4OJFSL+0dJRlZFLfc/Ma1enP7b1Eaf8cUKD4h1aLW2KaWsLY0c7ap+Ozn32a7VlzdDtztZ+4OM7ubPfMwk1LGaMPZuqPbWM7Xjmhqgqm5uNiSM7gqMw17Pry6Y3d7w6sPX5DDhEMKmOhn7JnBuYbN+hmr9Ypu8w7N+i0un3+GPI/kMNNtzlHG8PHHX+f29oov/qf/QFQRqxS1NaIumQPTcSRGwzRnxhF8UPgoi8OHW04Kayref+e7GR/3nG8veX31MR9+9BX8NDPnyDT1jOOeF598Fe9nXn78NYKf8SW51ijN48dv8/Zbn2WzOce6CtcKGfeDD77M9fVLrq9eMk09KUaePH2XqqpZrS+xVjxD+v0d+5srxk8+4Hh7xXH4iEPfc/fBF6X40pqLbsU7F+c8XbWc144peKYY8SkLQXj2EvAYAnvvOfhAimCxPLR/8T5ydzPBxoCDWBmic6xGg/FJ7PW1Qq0cnXM8X2+pZkUVFM+zo1OGs6pBGc3oDHfjzKt9z/7wAcNXrrh49AnWVVy/fMkwHLm+eo2PgbaupL8MpwnfWnl8cs7EFEXpUca9q9fXzHPg4vJXaLuOjz7+mFdXr9mPPZkovLIkhcd803O7n3DGcH7W8fnPOVwXcOv7444hklRCRU0icRyOhCz3xLjvGfcDVVthnCWGBu01YRfZHQ+ncEAZfDVai0IohMDNzQ0hBjbbLTYErBcjq7qusdaJh47VtF3HxeUF1lqurq4Y/MjoJwYm6qbGz5J8nGZP3TRsz86xlaNNrbjVKkU4joR54vX+NVZrNk2LnxKz90TSN4xks/dc3bxGmYR2GWdEsbB6e0P3eMtnjgduX93w5V/6FeLgsbeKbXfGutswmUBwESoHVmE2FboxmLOKoDwzA3fqjhQj810kG7DOUhnLtmlZN2taHDmKrXucJ6KPpEqjlWO7aiW7RSm6aqatPNW1eCdFkrTbsvizyBglhfcyhDsliIOLGVvyYZafnVp8BTVYJkYWtVPO6FK8qLgj+Vtu736N4XhDzGua6jHr7fsY1+HjTDF+ZXFBjSZhcoQMYZqYjgPTlPBR47oL6vUlyTRkZQgC15CJOJ3Eo0MhROA4kzCEeBSyZJYIRqUsuBVUE9N4g58D5niHShZtz8jKkDBvFCiLsirpjI6LKTvco3cZlQ22ctjKoa3FqITDsjWGjdY8MTU1StAyrQoqEE+IHlmRIyWtW9rMylgp3soiURlFVTuaphI+HRCNIavEnIW7kmeoa4ezhs8/veBi0+A/+IQpJmbE/0bUceJw7ghUWWHnEePqN+7xmBI+RmlQKfHbIoE20t6bhoy2YAxYWxZnSTg3q1qxXRnee9ry+LFiewFVY0Fr5l7M36oIxhlMY6RIMaVAKyaMKmtIhbisLMYqXK1497Oai6cVu+M5u0PHi917TLnDhjVZWZKpAIsETy64kPp1ypRvb/vvUqB8+OGH/KW/9Jf4+Z//efq+57u/+7v5uZ/7OX7oh34IkAfwr//1v84/+Af/gNvbW374h3+Yn/mZn+ELX/jCt/2Zou6S1WFVOerKYZRis65Zr1u0CewOB4YxSX5LEGc9pSQJs26cmHvlYkBDKqmsMB97UvbMB0+/3xPnGZUTOkuehz9JMSMoQ8YQgqJuG9ZnG3QKqJxYby/QxhLIYCxZO3IpqoRFrwghME+KkDKzj4SQCTETkyKnNwdwhXhY2EoCBc/OLvDzyH5zw9Dv8X4UF8PhiNYaP0/s7q4BMMpQVw1t03G2PaOpGzalgPIxMM0Th+MdN7evubp5idEGawztasOqW3N+8Qxt5FhjkpVzto6kDUlLFLxAuPLgOysIlDOmSOUk6yVliTP3ITCHwBQiPgof5UH00GkLKbGbZ8zsMD6I0kGBSRqTZMLIWiDSyiqU09RWHtRWNTTKoExFUoqRTK9glzK308AxzGAclas4Hg/MUzGyK8iDWWzePzWRLvfdw+/Ps6c/9rx69Yq6bdjv7xiHvqxYFNoaQgAipKgIxUHWuZl+mGltxD34DO+DED2R1fMUZzFJikpUC/MMKmG8uI7iFdMQ6MfxZA2fUmQBpBZb+mmeqKdKQhcRIl+MMtE2TUPOiap2tG1L3TT3SgoViCYRLUSn8LMn5UhWAZMDPgs/aZonaufE/baoJGKILNxHmTLTN5w/eaYT8zyiXS7eNUYGXdOinKHdOMbJYTtFdpExD1SqxukaXwm/w9UOZTV641C1JneyukxZSPUxS6GcEpgUSTbR2hqdFcZWYgaWE+MkOT9i1y8mkAtKK0iVuNHK9VziCJaJlQfS4HziWAAIo//NVgcFJcn5U4P90iIqP1AJchxJ4cg43TH7HmVatOkwbg3Kyj5nWT3nIinNRa5MlmiJEELZX412Ndq1khOmNKjEKc9a3Y87pyA7AolAQpNz4e+hBYExTpykY8LPI9qO2DSX1XeRo35qUywOt5x+nllAJ1G0aKNP7RRjNE1lWRlLg6XKkK29NxTMkjG1IBT38twFbVjutXuUUNBRQU+W9hKqjGm5oJkpYXJi2zVgNJuuwRRLhrycq+XzckZl8YoSp17uUeEsRpdLurM2Rfa7REcUHlLS5byrwmUBjE1UlWa9UnSdom5lfAIlFgY54wdPChFtajBZzNvy0mR88xIsho9KO5omoF1AWYPShqZucK5FGeFPyuCsyVn+XkjMp5P5DVf129t+ywuUm5sbfviHf5g/9If+ED//8z/PkydP+NVf/VUuLi5Or/m7f/fv8vf+3t/jH/7Df8jnPvc5/tpf+2v8yI/8CL/8y79M0zS/6c9c7AIsAo1dbLds1i2XWzjbOC6frLi9ueNLX7pltbqg6y5ZbyGGQQyInOX8/JyI4mY/0c8TxxmGMTD7zBSuIGs6rVFhpsaT0CQqvM+SP3O4I86eMEoE+cuXV7zz/ju8//kvYI3GaMNqdY7Wls2j53zwwa/xC//u38IYUNUg6A+afT+S+pGYTDHwCQJzl4fj4bYUNYD4tTx+zvl6w/Nnz/ngg6/w+uoFt3fS+umajpwC/e6Oi4snvPfu53n85DmXF48xTlpPMQVmP7G/esnt/ooPv/4Vrl6/4PXrT3jy6Dln2wsunr7F+fkjLi8fE2PkcDxwOCp8mhjDyOBHjFE0dYW5uEQbS7dac9F1nG+3aO+Zpp557vHR4+eReR65Ox7ZjRNXfc+MwgOt0eKI+GDi2oXAfz7uWOFZ55n1phafG28xURFXGipNt64EFSdjY8KkjK43JGW5mWMJMRzZ2cCdTtyOA/t9QJNoqlpWp8sgVmYGVczMFrjVe+F0nPxBHgxy8yyOrf/xP/4HtNaEOBHjzHZdoazCOE0ImRggesG2xymxO8y8+OSOy/yI1aUccwbu9nuGPLFuGmJO3B7uyuCpGaeJKcz0vQxmVVWTfOb2xZHpOHHsj4R5xk8T1ginAKNJKnPoDyijGcYJpSREcegli+n5W8+wxrDZbjHWom3Fzc01H330Edv3zlm/dcbm7cc0XcfhCpKfyT6iMIx4Un/HMBy52JzRVQ3RzxCjkGkVjL7wAbTCWlfcd+9B8JwiftihgyAGQR8xypLoMdlSbzxn2vD8ey/xPtDPB2jAW0/9SBCg9aMzjDMELbYDsxLDMPECSOSQ8GMqLdUJZyzBK5qqoq0b1t2W2lWk3S3Be4yTlWjQgegnwjTQJFmx58Jd82NiHiNTQNCAXPKkkmS9yKBlxBujH4jT8MZUvYQ+PvSduT8pxfAtypcfr5mHj7jbf5XooW2/l7p9hnVPCQpinFDJQFlMZRJRCz8t50QYR+Z+IPhMyhbbnOG6c7TrSEqho15s7aT0yPlEhMlhJmVFUAMJK8Z52SJwVAtujU9KggX311TZ0HWXRLMq8uCFiVMKE8BmqLJ45holviApKwIZjaFqKqyzhBRQGmpjudhseNq2dENAh0jWUvApq5ljkNDMUizoguaJ349Y1i9IphDFpb1ZVQXihJL5k0TWjDhlh3lCEXjr2SWPlGbCcLU78KsffYLPEBcr/yRqxxy4z2h643pqSJaYAiFndC0qIK1FsaNK68kHSWBWSjEHUYMaF6jbyPbccH5RcXlZYWsJmCQE0hw4vLrFVpbtk3NsY7FdBYgPDiVnSCrdMtBgQdW4JmKVlxZkyGw3hvVYoQ9nRVr8aazkQduyFIan/z/48ze7/ZYXKH/n7/wd3nvvPX7u537u9L3Pfe5zp3/nnPnpn/5p/upf/av8kT/yRwD4R//oH/Hs2TP+2T/7Z/yJP/EnftOf2XY1bz2/PDlevvXWM862a55eGroW2jay3xnmAGqYCaGnrsQSXKRl0I8BnzKHKXKcEocxcTh6pimitBj2tI1DFfLf5DOjl16cdVUxSpqoaktV2xOsfXLdK8qiXBAPVzW4qsW4Hm2tEOGy3HwhJnz0xUZaIlGNMaUf+WA7aeRKAZMyWhuauuXs7ALI3B3umP3MMEiLxkeBn0PO+OAZ/YgfJmL09KO4jN7dXtEfD/h5xFnH2faCrlvj6pZhnND7PTGLhfrd7TW7myv2JZG1bTuMNoXQJf1NV9U0zmKq0trxM8M04sPMOE+M3hO0QVU1DZpaS+prt9rQtF1ZFcgWFYwGUo5EP2GilYfcaDyqhO1pjLMFbQClIzonciUkwQAnqaZ2iboBa0Q0l4tiRQLxFHlRThQYXpcJ9OEDKFyA4s8i9GZBXZKSlaNSEuKFKEOQ+UIKV6UISpC6lAU1u90N1Jv5wYXO+HFmCiMVGjQ450Tq6wMasatOPp5WbHBvVGWMJhZoOFLUJKrAskqd9l+QLZEWhxCoKidKhoKGzeOADx5loF63bB5d0Kw7qrpCW5E4W2MEq4+ijHNG46wgDskogZIRGXUsRO7MPdrwxu2tFJV1ZB1kkksCOfmoxG05RFCBs0crQohMc8TZCmcURme0zbiVcKW8n5mmgZv9DcZmjFWATNIqyf2hkieRmOYerRPWKXw0GFNi7ZMQ2mNOzHh0jugkKGdMUkSlrARBCQUYSYUMUFbdUIbqCDkEhttbpsPhUyNaLqTrhaehQAlyU5o0kD06T8zDnvm4J/hEzhWu3mJcR1bmzfOa73VhKqmCgARS8Hg/E3NRZegKZRyJElRaLPnFA7us9lkQuFgKnUhSmpil9aByQp/eqyEzk2IgRYnXSDgyVUEN7sc0nYWrUSkJS62MwmvJTYraopyjriqwhpgihiyOqlpTGYvRxW1Vyz5qo9FZkLnluX6Idi5fD00Kl3TvcgOeztvCj8kFDRM1lZKC2yrOuhYfIq1zkD2+IFf3l/QEf336St/n+OR0au0ZsxRDqYio5XrolCEVuTAGxTLXlHEpSpSEzlp0WhHSFBluj7i2og6grREkpahu5P5UwtVJgrapKGNHpRKN9Zxte7ajw75M5KTui44TgeqePLSgUW8+zHxb2295gfLP//k/50d+5Ef4Y3/sj/Gv/tW/4p133uHP/tk/y5/6U38KgK985Su8ePGCP/yH//Dpd87Ozvh9v+/38a//9b/+pgXKNE1M03266263e+Pnjx+dsf7BL6CUpPN+7rPfxfn5GW8/b9EMDIf/ws3dgZA0/W2Pn4985jNr2lVDP8zMc+DQH5gD7OfEsfcces/1zcAwRs7Pz+maikc1WC2Otf3suT16mvWWrtmQ40T0gbOnz2jajqYRkpUPqThLaiYf0DGhjcVWDc1qK/bH1Z7sizpgTmL+NQ3kkiljjaWpK5x9k2J1GihScZrMAuFXruatZ+/y9PEzvvrhV7jb3fLBB18jRI/RisHPHEdJEB7DwPX1C/p+z831KzH1GkaM1lSuZrs64/mTdzGuRhnLq+trXl5fkdPM0O959eIDcoyomFi1Kx5dPGK7OcO5CmulUvchMM0j/XDk5rBnP/aM+x2hFCcJCFWDaQyPjDtl4mzPzqmahlTdO+h6A7tWM+TAfg6oWBGTJTtDrBSutjgLupKev08FWkORXSboSNDi6qi0pyZxnhX9naJX93ECxuoy2XhSLEmjSmPtsuJaMjzyKQE4pIRSCaMVOsnAF+cIWtHUWtoazhFE8Il1kiIdQibFzDx5+imyfzFRrYY3LvSw7zkMe2wE1zjWj9bM08R+2mGNxrmaNM+noDStoaqdjD1zQ/RCAI1x8SOVrA5lTLHoLtyHGGUyn0faTvrl0zwRY2J/7AlpploZzp894vnn3qd2hZvjPJqZ1q3IQRFGaGvLuu7ompraOjKeqKQlsIRkLpB3LtlUubofwI02dM0KnweG5Ml+JgRZ9eegiB6MNbz13iOZxHIi+EjwkRRBEanPxG3zcDWz293wX774RVarhvW6petqnDOYFFEpEfNMjjAMA9BgrMcYT8yOyfeEaWZ3fcsUAjs/0VUVF6uOaVJ4T0lphuApKehCzNQpih19jCUpVya2OIzsvvYBh8PhjYlLChOR7CiSoC2qZPyU+07nER32jHcv6XcvmSeNdS3t5imuPROegIqlLVUmIRRk0NmgU0QlcZ4dhp4YNdCgbIMyNWGpa5JGygZLQgjYYnGWIHtykgTqiCJqCymhElSqQtsO47ZAT4oD0Y+EaU/IVjJbHjLBy+RrgJVWdLWjqx1eG4LW5LolO4datwTg4CeUkVZppTSttWgjScs5FodVa0hkTLj3/VmKD2MWpcs96lnXNU3TvLEgOvVE1XL6iqlbjKKuGUd0XfHsfIvRho9eXaMyDKNfqDOFlMt9kfJwDE/S7vZRvKAS0gJ2zuCL/ULGiKVCLIuLJAWjyhUaUzhlWgKox4iOCofBKDBRE0bP7e0VddvQbQP1usa2jmxCsROO0tyJTq73wnPC0OKxVeTdt66YdaT56nPwFRHzjZ2cB6fs4c/+G1TGv/UFype//GV+5md+hh//8R/nr/yVv8K//bf/lj/35/4cVVXxYz/2Y7x48QKAZ8+evfF7z549O/3s09vf+lt/i7/xN/7Gt/zMt955m/ff+R7IFqUMjx8/p20aXNUT5lu8/5DVes3zZ4+4vu65uxuYA/QT7I9J1DEJpgDHEfbHyH4/M44JP2dRSAA5NSQUc1QELXbjq+0Zm+0F43g8rbaV0qw3W0EdbIsxthh5yYDYRMvQb3n8+DHJT9y+fiFqwkXZgaxAQSTTlVPUtS6rvvstBpEfj30P3MOXS7x1JnG2PqO2jnka6Yeem7sbjv3Ay6vXHIcjbV1ze/MJ0zSgUDRVx6Ozp1RVTdeu6bo1bbdGW4fSmtFPTNPAhx98mf1ux93dLZvVlouLJzy6fMJ6vRG3wgfMdxdlkOzHnjkn+hjYTWK8pYylqhuePX0L5xx11YgsVWmqpkEZw1HDAow6W7Fdn+OMuPRWqwYqix+FM2SxAq0i7ZcM5YkRUziV5ForKfMxlUWtHaYOaDNTW0frKtq6ZgQpTsr+ayvqFB/k6QvF6j2UWIOUJAWWkpYriIK0VEyR2i69/KWHjpKec1JyH+ZTz/vBhVawaVc4qzm/OMfUltQaVIIjRiy3Mqy6htDAPPkTClRVoLqOMM30iydClmC9h7yPpTiJIeBnzzTN7O52JTwSyfupHe15y+Mnjzl7tMFpyH4gp0DbNZAqOr2WUM1KBklLou/3HBNMUfKWZj+hAKuMMGrUNxJkQQbvecqIBs6IUmThlTjNxFxWgqCVrKazD8Q4M/czMWQOrqKuWp5eXOBQHG9Ftp/mgM+eZMWbJefSflHSiTchwxTwaSQbjx/FT6Wua7SxRKWprBHkUxuS1UTjUcVkK8VE9lH2x2ui98Tgscpgiuop7Y/0H75i8vMbiq04jfjjHr+7w/sJQuHF5Syhfb7Hzlcw3zDevWLY3UBsUW5FzmK8NY07YnFITWVVu6QJaJ8JyZPiyLC/YzweiT6TkyGME7M7omPhMiRIzEQmgvagJEwvIbLbGBUhjOIoTEZljcqamCdUjBjVgE6EMJJiIPoDUbdE48m5+tQFFySrNpbaGiprcXVDdo7UtmRr8ZUkOtucqLSm1oZaa0n7LgVELoRrbTQmG0mMLqTlT6MnqPsoEmttUaoVNU9p7YRYjApnj8sKkniXKBR+mjEZnG1YO8NbF2foDPv9kaiKEpGCOCzqoQdVirYKU2v0ZNFRYhlyufdBvEpCVMSYcTphjaiVtNL4HnqXuXotbtnrDVibi9orQMrUVY1GE+ZInBLH6z05RZpYoztJHEebskhA/GyCOMzGmDFaIlg268z5kGlc8XIK34QguAxWD/7ifhT+trbf8gIlpcQP/dAP8VM/9VMA/J7f83v4pV/6Jf7+3//7/NiP/di39Z4/8RM/wY//+I+f/r/b7XjvvfdO/3/77Xf4P//P/0dSlP5n3WxQSnF3+DrHfcXdjQS7PX/+CB/gcJyYI+QpszvKqlIrUUQch8zhENntPfOciDEzjzOuZCokRIIc1H2BcvHoMTfXItuUECnNZru5L1CsZCC0bU1dOyAzjlsePX7McNzLShshMgrCp8XJVUHlNFWlqStJxl22DIQ4M88jh4O0V6qqZpndJMlZc7Y+Y9Ot8WHmdnfH65tr+mHAx1cc64q6cuxuXxP9zKNHT2jaFc+fv0/XrdluL2m6FU3TStaIUuz3N9ztrvnSF3sOhx273S2b9RlnF4959vb7XJw/YuiHkt47FPOhwOQncs7MSQqU29kzT4Fu3VK1G569+1m6pmHddrJKDpK2GXNmGHfE4plgraNbn2ErLWZ4hcsX/ESMYLI5kSDlPJU+t0IKlAyl3yGrECvQrq0GlIXaWlrnWFc1OkZyGZwSWfJ+zH0S6wL1hiU8LOfSr5aVUi4kPK04ZfOcNkVZicqAmhHTKTJY8yZhVKHYtB1dU3F+cYmqNKMNJB8xyiBhCRm3asjacPXyToLgrMFgqJRjPPan/J2MEsVCiYxfbLlzOZbZe6ZxYp4mIBOCxzUVj956THve8fbveJe67bAqE3xPijNtV6MxdHmDyZoqadLsiePE/thL7hVR2iNhxi4nRuVSoOhvKFJSgskL2TUag9XSwm1si86aHPvTudOUwZSAihPzcc88BfbKwfqMz7z9GRrjGHc9t7c33Nze4L34ZSgjz5qt7otJExE/FV+SaosKqq4anM0iu6Ug3NoIgmcSKkk+UooRfCDPhuSUpEj7iWwqMpqw74nXe4YPXzOZDE9Wp+MO04A/ZvzulnluyFHaKwEgjeR0RE2vYX7FuHvFcHeLymeovCZnTQyeMNxBWXlHiqmZ6PfRQ0LHmRgOjPtbxuOB6CMZI0Gm+ogOkjgtnBIPzCjnySacDMdi8ESfCeNUCpQkLZW8PGsRTSPXNyMJufORaLfiSfKQt0BGpYAmU1lDZaRAMW2DahrSakU0mt5AjgEXNJXWNLq8VmtmWNKKSuFRknqVOnF63niuSoHyUFZ8Gl9LEU/WZUIW1+VlASkLDo0fhbe1WiVWzvD88px5mvlEK+akCEri9SitoU9Lq5VRmEoL0pEl/iJlQcXJCqNN4aoVJZvKQv7PCj8oegXXV4GuM8w+o2JG2QRRyNlVJc/ldJzxk2ecB4wGQ6a2DVRGWm0ZIdbGSJom0qyIM9g6kZQUKGdjpqkSc0gMYTmH39C1Woa335Ltt7xAeeutt/j+7//+N773fd/3ffyTf/JPAMRlE/jkk0946623Tq/55JNP+N2/+3d/0/esS7z2t9qM7Wi7t8hJ+t/aWEIM7PYzN1cHfu1rr+kPR/bDzH6Y2Y0zx9dyhve7QR7cLCm8UzTMM8TsMFYSI5umEtlZsxUW9eypnON8U3P+6IKLRxes1jWZzPrsnKrucHVNVjAMPVXjqJRlHEdi9Kdck8o5jNbFTlwgWGckr6S+OMcYWG0qbEFQ6ur+cqUU+dUv/ScqK2qlpmp5/OgtjLYYI1kbSssxKaV4dP6UVbvBWsf+cODq+oZxHJmGnpgT2lma1boUXE9p2hWr9ZYQPId+R388ME4Dr199zOGwY393Q5xn1t2WlOH67o56dU0o40OMgZurl0zTwG5/zTD2HI97skqcXz7i/OKpoCRVi6squWYJjtN8cjw99gd88MS6OuVxWGNo246qUdSNJgQh2yqTJGkzZ1LITCERS98cm8gmkeIk6MosJJAqOmyGOmXOcqJ3mfG4Z+4n5r5nmCS4MBXbf1tZUAltiuvDIiPNCZVzMQ9f4Nx0gvMVFDKaLiiXwYCscFXGaiHCmcYUB8kkIZQPNtc0GJ0kh0eDmSJNMjzqtjinsFYRbUXIcJt2eJ8gaawxVFXNod2jrZHcl5zKfmhSlHC1se8hJaL3zOPINE5YpzFWs312TrPpePTZt9k82VCdrZhu9+w/ecHJyXK1JqPZHXtqa7FtI+dv6DmkkVlFqvUaozXrMQicHxIxSxZPWkLUHox2fT/w9a98iXplWV92rLsVpq6wdU1VHG9jDoxpTyaSs8eoROM0beUgZl58+CGvzSvCFKnrlkcXZ1itsErz8tVrDocjWcnq+eJii3GWSjkqVVHpYtZlDOM0Ebznbndk8jN3hwPWGZq2ollVtF2HOxqYhVMUYiLPQUIJkxjGzX4C5UhJMX71E8LVLbsPPmHY1PC4OxVoL3/t1/hk2jN/cUVnJSsHBUlljPJYM3HWDHRuZF/4Ynf9QNa3XF8NoCqyasoqvxQoOeNzaU1OAZMiVZ4ZhjuG/o5hDsQE4fjvcK4uPiManS0qR1QOnF16VpvE03c6jFXcXR3Y7Twffn0QcteSs4Nwt+Sen7A2slpP1OuOrmmKa6l6YxKrKsfnP/sujDPtVEz8ci5mhkqk2znTj5IMbYFN3fBsveGiW7GqaqI5EmMxOdNaXFqVXOu2bamqShaR6d5XxDknryleQCGEEw8lFQXQOE0yRoeAVoU3VhBum8BEMCnRas2zTcfUr7k53/D67sCuH4hRkXQmRVvu8/utHyLDzp86QDpHFJngKeiUJhd+07pWnK00bz+tqSrhNApXylN3xcdFJzBCrSVBnOVYrbWChumMnzL93cRxtGQt2Uw5J3IYUHlCp55+0IyTxq0uyXbFi+Mlr242zN4RkvlUoSfP7OnJ/YaC5X9DKp4f/uEf5ld+5Vfe+N4Xv/hFPvOZzwBCmH3+/Dn/8l/+y1NBstvt+Df/5t/wZ/7Mn/m2PlNrh3VrIeflIkFLimmK9IPn5q5nHmfmkJlTxsfEMEdiTBwOQTgACemxqur0b2sU1kiiqqtqkWpphUqmmFk1dOuObtXSrcTY6uziEdrWKFOBAh9mTFBEo5inmRQNrrLElKicwxVY0WiDskKMIoOrKqxVtK2VQDyXv6HCv759hVGKVbMidWJ4pZRCZzFBy6nk2GhF23Q4V5GUEGnv7nb4WeB2bYS8aZzFOkdVt7hKOCdh6umHAze3rzkeDrz85CPGoSd4SUHtuhXGWObgGcYJVw1SdMXA3eGOoT9wffOJmM15T13XrJqWrj3D2UqknCiWLA8fYnFo9eyPO2Y/07hLtJZbVWmFM47KgXMyUKQchHeRWWCJk5oPVDE9y6RYuA8RVMrYaKlSpomJJkOr4TBMxXFSUJ85SP6JUVqC8nIEVElJlQFi6choJbLv5dlVZWWv1bIn8nMhTotNfwKMLiiAK74IZSB84x63BmUN2cjZ0iHj0HRVTVVpyerQljlmTFaoMt9rrUsukRVZeBbCr2aZSDIpim29+EEU9UhOkl/kNO35mu58xerxlmbboSuLnyeON7doa9DGUrmNoIu9R1cQa0HLxhiYSASTqWqHNhaXHSpECLMUJ4svQ36T/e994PXrG9a+xXWOrpawM12QFGOMZMvMhbSYAkoVg0JrCMbQH3tyGuial5ydXXB+dk5oGkLX8VppIZeqws3AYXFYVWG1w+kaZ2qstXifCQqGKTJMnuMw4ZIjV4ZZa2JToRspUgNyHcM8k4lMPjFPI36eMMqRI4yvr/Gvbxnu9szmzRH9cHvDeP0C+0rTaPFLgUzWGWcCtQtwAWmVGfujtG8PmRAH0o0nZwvZnazfY+E2TKVASX7G5IQjCQoSPOMcCDEz9R/IfS3TnyhCJBgIP1j8pePxE4fRjvE4crjtef3Ra0FmEpDvbd1BVvN1A6aQXJsoPhzqU5OWtZbHjy9IxxHuhIw9By9OrqYQZRflXEkhb6xj261oq1rQFl2ePb2gc4VfgpJ09+JbtPBRgDfaPYsk2TmHglO8gy8tj5wkzympkqOUKOR2UEkSr1eVY9PUnHUtu+MgJNsiGMiL2dqDzftMHBLWyUSvkIErRUhJPkMh3MfWKVa14vGFoWsNqpK4i3nOuGpBIjNKF8+cgtqknNHGoE2WdPuQ8Tnip0RUksqcUiKHCaNGrOrZ95bjYLDJkFzD9aFld2zxyZByQYPzMqoh0ugyd/03ObN9avstL1D+wl/4C/yBP/AH+Kmf+in++B//4/zCL/wCP/uzP8vP/uzPAnJD/Pk//+f5m3/zb/KFL3zhJDN+++23+aN/9I9+m58qq8K8WOcpuRhtvaFyW3xoSarGrR3decXGN9ze3OH7keM44724gApMGXDWiG/HqqVra87WK6qqYkiWtml55+1H6KJMuLw4Y73u6FYdVV3z+NlzlLaMs0C9IUWG8cDhmMT8yjjWm44U4d333sdpRZwG5mHCDxOH/R4/z9S1LQ9NIkTPNPdv+KAopTg/e0LtajbrM+qqoW7XAk8rLQ9TTmJ/TEYVm/NVtaJ6ZDlbbfjk1QteX73iZneF73u6q9eM40jOhRWeMvv9NbvdNXNxS62sw1nH++98lrpuWK23kmJsHSEkYki8vn5B3x/4+OOvkHKkqixn23NRAzVdgckbtJZJcyGf9X3P1e01N6+vePnqBePQk3Pm7c2GqgwuqEw2kTl4/NGT8oRSico14i0zO6wyWJVFIj6VFkr0aAKJEpOeFTkGmqhZec1ZhslYXvUHboeAQhNSZJLACwyKfEzMfsLYCmMs3XoF1Fgt5zmGKAZS1hL8RI6BlTU4I+6by4pOxIOcMp8aU6GVItiKkBODn2ncQxcUGLP4mfijDNqutESMFsY9Yxbqbco0roZa0c8BbQxVXVE3DU3bMAyJNEvmCMUe25uZfjjQbhpWlyv0uWYzrzm7XNF0Nefvv42pKpKpiTlz+/KOu+sj+7sRlWXyWdnH1NZQW4fScPQTsXXYzSPWSFvAZgc+Mt7difohwzgNDMOhSCoV2+4RVTl0hZAZXVXRdSvOzs64ODtnuN0zHA6kPKJMxHQBVPHkyLLY6FZr2k5R1xtyUtR1h9aGq+uXKKXo1mLg+PZ7T5m8wOlt3YhHjdKFOJkZpwNhiOx6QU7285EEtBfn2LbCbRriek3f1mzOHWmEvZ8Ybm84/PL/SjQw60KQTYl1drgI/X/5gHB34LDbMbVv5k3FaWI+Hhl6L3b2qdBS80xbZ2yXmY1lipph3zOME/1dIARNCBNkDdlgkSyjhVg7Z+FUxDxDSvQxMQ4wDZkQZAVfNYNMZFkQSZMXI7OEzTVpqoifvaRSGj0HUj9zvNqRY0YXBQiUYLxSoEyNptYVOTvaTSQ3pYh+UKTUVcXnPvdZ+rsdV3yCSQGXIvXFObpteD1MEq7qBa00OtA6zdNHW1bGSB6PEdQkT0nUu5UoX0KKxVHZUjlHSomxF2m3Lci89+FkgqeCqFqOhz3TPLG7u5MxwFiULdk4WRxtYsxonxiGCT0HsovU1vDus8cS49H32JRQIWOUwX5q4WGtxjWGvChqijsuxggamBPna8v5uqarPLXLaA4oNJuVhBmia0ylgYQxolAzlSRXT9mAjaAGnNO4WhPnmRQC024gzDNDyoQcmPMeV0WaJjJ7RQiKj15EdnPkP11V3A41x1kQQEMUsz1JP+KNeuUb012/7aLlt7xA+b2/9/fyT//pP+UnfuIn+Mmf/Ek+97nP8dM//dP8yT/5J0+v+Yt/8S9yPB7503/6T3N7e8sf/IN/kH/xL/7Ft+WBApRqsVSNFJa8gqpqadsN2+1TUs4YVxGp0bbF2YbD4UgMjmnyjIOXlFkfxU7bGeqmomlrmrahqipMVVN3Kx49fYYxMjlsNme03Zq2kwKlaVtEViwwrw6hsN3LQKFEYmmM5fzsguQ9x/fvmPqRaRjZ314zTeIlApl5Ej8NOwmZdNkUilW3oWtWrFZnBdFphfyZEyEFYgykBUcoAX1iKuVYdysO3YphPHIY9sQkUCZKcbe7lSo+BA6HWw77mxJbbqi7DU3dsN1e0DQt2+0Fyhi0thz7oeS0JLyfGEYh71bVGusqVt2WVdtR1y1G2YIiiKY+5gAKQpgZp4G+P+LnET6lqQdpXeWURD2kkX6zsihtwYpUziQDUQysUzFDSkqjciYUWnlWcnaKvqFAt5HJLxLvdAr3Ugi6E0k02mJMpqrFECrOVhAyBcZajHMi4cwZZyyVlaBJlBLbcBQJyWNSIKs/pbFWVrtYReXefDRTjoQcsMkgSc6FGq+FY6OKb4tM6hZtpOWBUhhrRUJZVUzTyCJthGJeVZQEutI0Zy260zShZnu5oukkqRhtGCZB6ZKfhRishEijMmgMVlsqY0g6MeeZZCzUFo2cd1WQjjBNEIXI6GfPPHoWG/6H11obTddWrLuG9WpFU9WlJ++Z5gmY0DmVkHd58uXeK+0+DG0rNuzWVqSUSziiwVjLet2RgXHygiqVLCMjGk6UUkImTaGoYZJA60qjqgbTOlxbk61mVolJZ7JRGB/l/r96jc+RIY3oLEN51A11NozHA2HsmYj4T2HiOUnBG5jRREwKgmzlUaTaSZFCIswaPwX8FElzIgVBCUUPGznd3WU1LehyIue5fEbGT4p5VMSoijw0iUQbOYe5LP7IiXnUzIOSgMqYUCmjUhK31pBLe1IyblJBfVRKBJWZh8jUB8ajh26GZiLF7sEzrWiamnmoCIBxDmcr6nWHbhry5E+iWMEgM9ZI29uqIpvWBRUsDpspFbPNItXWqjjHmkxyQZ57pU7S+mJ7JLb4CeIk4ZTRiyurtJYUThmSvuegSWxEFASpIDZdXdPVFV3l0GFGZ+FBf6PJY1EOPiDOL55XBnAmsW7h8kzjjMFquT5KJbRKaK2QoVSe5xQzORajt6yYA8QgxYbJGqsNWUvrO6dCgg6BlIvh3gO7Y63keZm8Z5wi05S+ofgoZxZVvLrKQb15jHz7238XJ9kf/dEf5Ud/9Ee/5c+VUvzkT/4kP/mTP/lb8nkCS48LF1K8DVA8efI252ePePToMZDAwDQcmYYjr1++4rDb87WvfcB+t+fDjz7mdrfng09eScvFGs7P1nRtw2a1oq6EoPjs+Vv80O/7/RhtpI+vHUoZxkkcR7MSJVHb2nsYOkg7KYREzoq269DW8PTpE6L3/MAP/BDjMDANA/v9K6bxyG53yziO3NzcSG9Sa/wM01QmTK15773v4fLsMc6JNK6uDeNw5Hi85Tjs6Pt9cT7VReqlmGchjxljuLy84OxiS7desT/s+eTFCw67I+Mwo5XCKIH+Y4i89e77XFw84smj51RVgzVig17XLc7VOFczTSPTPDKNhyKTLMSwZDC6oW23NO2Kpm7wXtQAMQZCmNgdrrm5u+bjF19nt7spK8F735HTvYMIHmX9ZYXIB2TdiOPhWiSUekpMGsaUCVYRdcWIIagoBQ6Z6AKzV1wp2BvFqBQ+J3yKHKaZnMEVImkCkk/kOVE3Da7SXFzWGKsxxuPniBlSsf93TEoRvWe72dA4R9WIOVlWUfwsVBE0q0yNld62a2TlpwLt2dkbxx2UJ+gZZaVI3YUJozRN4yAaSIa61liV0Td78uyZgsfairbpWK83nJ+fMw49fXHWVIVQvcgsV5cb3v7+d0W2niJ1K+ZpQSm8H5mG15KbkwJ61bDuHqF2ERs027MzuqahqTWjH5mOA1FrvJaVptARBtIwMO5uyEG4K8M4czyOwjWwhhjvJ+t1V/O7vu89Lh894u333me3O3A49Oz2e6ZpZLUxKFMKTGWwxrEf9txe9Uz9RE7w/jvv0TQ1625FjJFhKvJtFdlsN1R1zTx7CX089MKnqepTUXe3V/SDwji5XuvtlgQc/Uy2ilQLUfJ2f2R/zNhZ87ypSD7w8X/+NXbHHa9uPsFqjdOK9x69xVm7xkUFJhMvLWztG4N4yJl58WfNkZxLK4wZZRxVW5MzzENi3EXGPsIsnh+mPBnyvKdTcUFGzArJKBWLugexOs/61BrNhZSsrC5eKhZIKCJhSkzHQBg9qbZYMk6Jn49PiZCARd+yIAU5kGbY3wTG6ch++IR8rciXE9tH30vVCoKREYO64zTw4fUr3n33Oe++9w7V+ZbsKr52OOKnCaUdVmUam2lrS9sadEjkkAvvRAtCSCZlua+UkYkZY068v8Y4fIzc9b2o1saRylgq45j9iEqZNHrwCZs0c/DspiOhacltQmw1FUyREDRJa7S1wq1yjlXb8uz8DJMSu5trkvdYLfyVN57rEMmzx9Zis6+sI6bMMGZWLnGxgXeeaD7ztkW7WuzqwwwkpiELAcZNmMrgsmHYJeZe4VYVIWm+/gFMIww9rFvN+VbGTEwkbGtSUKhxwERF4zWVgQ5JT6515pG+w/mJ9/wT1i4yHR4xJsNAYVQAJrqi3pJWDyqUK/pN2LO/ye07Iotn6R3qQoxaVmFGW3A16/W5rFZNprYVc1Wjsmaz3uJcQ9/3bM8v2O0PXDx9LStdpVh1LXXlyt8V5+fnXDx6zGZzhtaykhXrY6iVFeXIpzSiWov/hVb6tKpOWdj+CoO2iqpRKGUxtsY4jfcjdbthmiaqeiv8BKW5vdkxTTen904x4IMvHjFSVQ/DgcPhhuG4Z5oGum6NLTJnhQRfLbbKSiusNqw7aQ0Nh57Ze4IPZVAT3HcZcLQ20t5wFUY70LqEGA6M08jxuGcYj9ztbzj2+3IuJGDOe8/heAQlcHbwkZgifb9jnkdud1ccjnvGcUBQl7pYy7tyrsv5VLKCWd77xDRRhRBnhcmOkr5tZQzaKJJJhCSunlrLatgjfgbHFJhVIhaLfGcVVeFnmLKaWdzIFcJvqGtHt64wVjNNjqq21LXCGokEmGwiestq21I7R1VZIZNp8UVRWlxHcxLlkcZgqhqU+LfYT3nemKrCOCVBglmuPSrjdS4E3YwqMesJippMoZ3GNY7VtuVs3nLod/jk8ZMnxcV8TmFqS9VWNOu2cHWiZJEoJTJ8JeZRGUXIIuFUqvCj0CidSQT6eaIfe3a7W1Rao63Bti3aWsJ+FA+JohhS5RwrrdDOYSsryFDZnKs42zxhtVpjlSWGzDQKzB/i4g9xz18igrE1baMgWlIQVCH6SPChZNrk0+MZQyTqIC0DwFlTYhksKLk3Uvky5dkRrkwmR0lC14UQDRnbODG3qxxqTrjGUAWNq0EjHkVUgVxHjGvkXs4VadMUUXzZr5wlQJNlrBCDuoTcw5NXxDmhc6bvYZoUIZUMZLWgyEuarjCh3pgskhAvKYZbWmXhuKqF4K0KlwSylpaKtG0UIXBqiaMSShd7Ay3oy4NRubRwpDj1MwQiYx7Jek/OFe3GU7Xl1SkzzTPeC5JqrKWqK4x1pOLVs/gNoaEyVhAFpVnC6rTWwrXQunj+RBRa2qoLUhiX1mbAh4AfpXUtqsksSETMp0WuLuOKYhkIJLQzl/sgFdWSKAdz8YGJqBSx1tA1NaOzhPwmOfbTW1yeByXnOSY5g8aA1hmto1gWWE1eUFOdyEaBM2LNbyUKIsZMPM74AOMhczhkXr3KNK3hdm/l3rMdc+pISaFmg2VgYw/CVYqaYzAMwTAlydq5WPcY47jZ7djPDeNULpwqhpD5AXCiyh8PbrlPc45+o9t3RIESU2KexflSawmhktWpQltL025RWtBwqyqsqVl1Zyjge7+vIudMPwyM08Tt7kBM6f5hANqmwlnLZtVhbIWt1yQMKRumacSHQNesUEqSXiWSfhRGu5GLY7Qi6kyOQphTWoyPZFNkU2Pqiov1OUaLG2WMkePhcJKAfulLv8LVaylQcs7s9jf4eeTq6hVzKRDG8cDhcIuxgt68/ew9um4tE35RkWgtLYNF///o7BLOLli3HfvDgS9/5ctM88Q09TRNS9t0hJiZ54hPYEqSaYyB4XhgHI70hx2vXn3I3d01N7evi2JBF5v3zO6wZ5g85+eXrFYbUgqE6Pnwwy/TDwfubq6lsFRQVzXnZ+esVmtcVZEf8DEsmjUtQWWCFkSCAnVqk6iqDF6swp2y4hZcGaLR5LEh5IiqJpIKjIzs5p6r+YgHQqWoW8MmW2ojHJ4QYPaZvs+YLPye1arh7Kzl8dM1xmmU9mgMtW0FGs+J2TfEmNh0W5yxGKNlADReArkc+LGYek2GnDSuackpMo7p5D9Sbg/q9RbXKdq6FlnzNBJVIuhAUImYE1VdgzGCAuWIbgxuVbG6bKk3mrMnHbnOuE8aXn/wkmmQVouxmvasY3W+ZnO5ZZ5mIc0WsmwOYuTUdCspYPsJ46T/XVVglSLnmdGPvL77Oof9gVcfX3H2/B0udMX68XPasw03twfSYSAWsqYC0JqqqWg2K+q2wT5obXVdx/d+1/eJkeJhYtjN7G96pilIC65Mw8ug7MdA1265WK/pdz3z5PF9T5gGCIVtSPGAMZrj/sigepq6wWjNqm7ED8NVDMPI4Xik7wemacZa4Qn1t3tCTvRpwjSWSteoHDEGzp9d0FUd69SR5wjmgvWgqc9HQpwJydM9sjSd4ex8g6tqnKsYcs2rcD+A+5wZY2TOAZUDJo4y+ZvM4DPqAHOfCGNg6hUpGapKlXZncYsjo5QUXPfQ++Lga4UsW9p82iR0Wn5PF7Ky3K+ZuXgaGaJX+FExDDO2UWQdUDYJHy9BKtLTvPyZAQwpwTBk/DEwvJpgF1FnPedPv5/V2TKGR+7u9gzjSNVU1I20y6M18mzGyBwCfpqpKsuqaWhdRaWNWDSkKL4/zlHXNbP39H5GJ5nMU0gkIy1zUqI/9rJo6nu00oKsqMWpVYoYUxytrTKEHDCpkM+jFDoxJVRZ+C33VoqBpBKRROUMm82KsT8wnxxuP9WuLoWh9+JonpRDwm8VEYPS4rU0+wlXS+6TrUrwn2S7kJ3FOIN2xe/ER3ZXR8Y+cHjpefU68u//40xyFbQt7fnnqNfP2ZkLojI85hMumx0/8PaBEBV7b3k5tFxPDcZlrIXPPL3CxyNg+fjuEdcff1aeOwVLb0xTatuFkVIqlv/NtXj+R2yLAiREYSorNCnP5FSY30VhMfuEDwpdcg10doAoPWxVc3beEuODLAwytRMEoqprtHFoU0vxkRSSoRNP+0BBG6oSx10+GFBo40BJ7zelxDiOJ5vlEIIgF6nGakNcosGRFg06ox64OeWcePHJ17Ha0PcHWe0FCYSr6wbnXGGuu1NvfYFR5Rm8L1Ak2ROcsXRNw+PLRxz6A1fXnhATx2Fgf9yD1ti6oaoaMSzzE/vdLdM0MA09fX/Ah5m262hpBSo3jqYQFLW2WFsBqqAlR3b7W7yf0MVqumkaKldROcnDMMYy6fsgKoXBqJoUIspHKUx0xmbQKZHHmRQyoXA1MFb6x/k+eCtOscgWAW+oVXkQc+Zi29E2jmiqE9Q6+0xjE7UzNM5wvm3oGodKYNBcnm2lH15srkmeaYIQEs4pufw6oTTYyoryxwaZSwAVdOHElMRi9ak2bubeDlsBztCerSUSIUVChBwSqVjHKx2pakXVrlmta+g8FYqGhsfTBabW5OgZjz3zONBuarrLlmotBFdXi+JB7Mkjsw/kbDC2oVIV6FruRaUxVloHw3wkpJmb3S2HuyM3N3u8umIKhtw0rGdPDDKgm7ZGW3EfLleH84sNq/Va0IvlsBP4WTFPGV84FpJBIyvcFIMoCrQmZXFgjnlgShmrrAScQSm0ZIW92Pdrawu/Iks4XHES1cbgXCVIUSomjoX1QGlnGK1wVQdOQ60wWSSezaqhthVpCCiX2DxuqX2iPp8Z557Rj5xdrOm6Dav1CmcrmnqFmgzq5n7BWSx1JEcwIyRoQKUsyc/HnukY8JMgK0pJSrXKiIKjvJNWEjKXl28V5a90fVT5foHiT6qPshOF8LF4vWh5iKDI0pPX6CLkNwVdiVHf1yV54bTI91QU+3WLIgt9941VdYyRu90e7z3b7ZambdHGEDIljmSJnEiySGgEmdRKlCzLYnJpVyqlZAzNkAmSMhyl2M4xMQ6jGCtmscp3SsIB1eJhBIVjkotPlqFrOqwVAztBAou5Yc7kacJYja0dumQfLWoyZw3JfQtjs3zPf9MKklbEpBhnIW5XlcXYXPxGIjFlrLGiWnQGXVls16CtRlvDbCa8KUydDK2zrFvF+VniZsi8vB4w8xGzP+K7x2jXcLm1BFVx47cc54aXh3NuhjV34wrrZqwNjPkGZzMXF7fMGB7d7DnOLUffFnQwkEqY54KgvFGYfJtVyndEgSLPnhKL6+Spa4HGvBd/kWnyhdwjJMiQkAJFa3JwcjOjMZWmW9tCepKk3VNEvVJYZwufw5VnOhXXVFNypYSYpJShrisWEuJydbQVF8QcZmJMzNOAtVYq/mlmmiZyBKut5IzkhVRVRKEP4n1TSnzw4ZeE3KXEFrx2ovXvujVN3ZTwq0YKtoWMGhe2eIFEtZKVg1JUxmKbjudPn3F9W3O3P4jB1thT727xKRDLgA6Jcey5vv6E4D1hnqmKvf12u6Wua7bbc3GkbdZlVYYQaaeR/eFOTN/uroHMarWlazvOzoTw66w7XVsfpO0BQsgyqiHGGcaMdWJgV6cEOjL1UQLbkthzi1WiFHs5LQhWIOaETwoVHK1RxDqSjLgAhxjBiOxyt0v4kJjazKazbFYW6yqMtagIJmsuLy9wDuo6k5MmxsQwiPw1ZgGgU84oq3CtA+WlT1vcIpU35KiIJcROqW98nsWdVLxTtDGsL84YfcD3wsT3ORLiLJOJidStYvVoS9060mqiqSu6uiWYR6zOOnKaOewO7Hc3NJuW9dOOeluVnJ9KLPi9xDfoYRbyo3YYp6i1IoUgwWcuk1PgMO4Z/cDr22sOtz1XV3cchsTd3UCuas6GiY1WKGOwq5bsA9oHjMoYk3nyeMv5xQXjXST6XI4Zhj7jfWYak3w/5qIhj8SoMEmjdEXKiXGeyGOEeeLJxWPapoGSWdQfD1glHjquqrCVKym3Uei0WrwyjLU4V8nYgExClDZXzhlrDKZy1OdbgkpMWlbMqEy7aqm1Y56OGA0Xj1Zk5bjEsO/3HIcD52dntM2Grl7jTM26PSPvEtyO9xVKQu6HpUAJp0WqJFcPI0Mf8FOiXmkxLayWImLBlSBpjSo261KHlPZUVoXQKs+ktK3KsLDUY0naHaoUKRqkdRozaZ4lZiBHLAmbkVaBX57RUpyUwk5lyLGQPhVEHBn3ZoESItc3d1TO8OTxJd2qQxuxqg/FmXcJTzRas+6ENK25l8rDpwsUWWTqmMkhodUsLZ0QCd6jlSCSFo3TRkbZxe01S75SzFmUOsqw7trSTpTWjs8ZfEKpiCdgnaW1SuJQik2FUoaqMqRC2v80ggJybe1yko1mjhBDgiy/62zCmETKgRghV4LMm6rGtRXt+eqkYNI2ocZlDFFsGktaG548gbuPZl5cjYT9nlS1VOeadlXz+TNLVBWv/QUf7y/45Rfvc+jPGaYN2hyxdmRUX+Ryc+B3vHcNGl598ojXUTMPK7Az2QQyDjEwuOdUqTKK/++6xZNiZp6L3XjOGCNk1Gmai9NnSbdEE1LAR4kZRymUqVBKM/uMLkF9Icx470+McVsSdftpLlyH0kYS7pnAqEjFHfx8gpy1kZ5oLiX5HKNU8QDFwfA+mAogk9IsE5Uq7qKnKHZNTPcBckop2mYljPFODNjqqsW5irqqqSrJs1m1K6yxWGdkoO4PkrUy9NKnVRC13EA+6vKwGM7Waz7/3mfYHfbsjwdCCNzd3pJicSg1ME8jQ3+kshXr9Zaz7Tldu5LWjHO0TXfaLxDFTtPOzH6mH+7QKnN9/bL4EiRCFBVARsOpGCtLv4VrkjJm9pgQMQlyqAjJEPJMJou0EIG9F7H+ycExlUA8H0vchJXWjHFklUgmYaww/5cVUmUkX0ebSO0ydYWQ4rLGBYeZLTYpTI4oJhIjmQFrDLrSzCETk7Dpc87MegQzg5mYY8SnhJ8DyRvJT0kSDNiaLQ+FxjkJAjH1Fqwh15bZR/wMKju0cWWFGPDFVjylVoT3tsGnyHEcSDliKs3583Oas5rqNlO1Nd1Zja2UFOZR+t6m+NScU8ngmlLJDfGoqgYFbmVQMfP66iVpgO36AkeHCR2puGnEfY+3OzbvPKPebDirV8RxYr7bCSpVW87Ozmjqlln3xCXYQCkoaOI09OQ4Y3XibNuBbrDrjHEU9NOx2W6htijvaLuayllMtuQkRmPOWdq2xlbi94OqUEphC5JYV7VwrUpOSy6qq6YVXyBxMV6D0URrJNPJJHyaCTlQKYvJmpV2WGDdKOYoAaTKgnaFSGksqSQfhyCCpoebImEIpOwJyZNLJo8uy21tFJXVmJQxCbRPqFkJ182k02OjFuYrhUOxuCsXl1FV0pa11PbCS1Hy+qBLYa1T8UNJYjjmM4fRowaFRzHGzBSTfKV0oh8sQbm5rORzloIpake2DmxLfrDgiilx6Hu26471di0FZCpePIjSrXEVphFztZTEgt4XT5MY4zJCiIu2MRitCUEQwIWxRs6orKhcJXJ9W2GNOSnIBFQSAv8UA7MPjF7MNYUnIIh4DoE5l1Rhq9nWa6ra0bYt1hmsk/s2FJfyb0UYdVoK9O1WUVeKqnUcp8Tu1pd2U4VWGmsVxmW0BeUUymlc6zCNRVl1qiLFTDKzuaiwFm4+9mKOeaZ4OhnePViuxyN7/5LKf4ybR4bjmjBv+PjuOTfThtf9E3KoybkmREdMgRcvJ8bjHU9XX8bPmYvzHX1YcXOARAlspZZxnvs243/r9h1RoIQYRUVToFyjLVpHhmEipXgizSql8DEUWFwMsq0V9cc8C7JgfMb7mXmeiumWwqUKlNhVg6y25ImT1aYx9pSlM89FMlmUQLYuAV9ZfhZiPMGfWeT2xKhP6ogQorDoEYRmKZRQmhj9G8ddVTVWWzabS5Hk1S3OWjGWcxXWWtq6LSmdWnw0wlw+qyApKsuYhBgzKaVw2tBUNdVlhTZi3X5zd8c4jaUVY7BGE/wkni2upqkbNust680ZbdMWz4EKoy3OVtIP1+IRUlUVq27FPPWYwlpf0OaUFzhd35MfH245C58ggsqKlCw5W3wU4qsS0g9JW0HNlKze0pKcmhUpqoJ4ObQuURRFNmirolkoJknOJKxN1E3EmoDRgb7PhKDRyaKjRSeNSmJylrNI9sTsShGikBRVhpwkyE4miiioR06CTMVcyN6Z2Qds+NSslcR2P8xeYHaNGAx6Qc+U1uQ0S5LuEk6XIyqnE9y92LUbo1idrag6C3rEVo6mFZ5M9EEGYC2xAtZYQQMTqBhF4ptBWfF2aVyLyordYUcIga5ZY6kxsRH5/uRhDqTjSGMr1l2Hbtb4YeSYMm3tWLdiemiMfWOlJY+YtFr8PJOzBDE2bYt2kGsPRvyPtNU0rUEZh/LCPTLG4pQiJyM5OK5831rhaFnx4XFazBKtkXavkDShquS+MbZitRbFz9nFBVkp+hhIOhMtTHFkDhPZC3eh0TVWQeUS0U8kDxiNMiKFz8oQCzfCBwjxzUttNBgDpMUzR5fzIo6iOitcjFhVPI5U8dRBIhXIFMKrKs/AsghSBZUttFlNMYkUfsZD3kDWimRAGXHsWXIblFKMQWE9JGPw2RC1IWqIJt8XKNy3k07vaQw4h3INVB3qAfk9UxQtStG0DcZaaaEUJKAyjtpV2FpROVekuLkg4sIHEbBa+HHaRLSxECSqQRVUxFlXEtaXAsUWkq8+tYEjiqQyIRfTvaK4i1na7FrrEjsgpoDiMi2uw1VdnWwqlIqg4r3/kVbFFuB+02UfNq2m6zTtuqLqk5B/syEnyzIkJiXnI+BAWZKpUMoRUjGxBLJyYDS2rrEefA5EpWlazWateXSmmHJiDD0u77DR4efnzKHh1dhxiC19OMNl8VoK2ZFzYn94jIqWw/FDxKBzoq4CVmeiFuJuzuJua9T84KqWW+/bLFZU/rTJxP8fbLvdjrOzM/7yX/7L1HVN17Vsz7an3qFZpKFx4YY8ePAWmLO8lzEWUKdwJlloPHD8U4v7p8TSywO4MJRzafksQFY+ISQLg15URfJWC6flQaNOPkJ/Kg68vBtw8uEAmMaRoe/vfw/p8Vrj3oA2JWdFndAepTipmxZb/RgLo43741w++X6SyIQQ8VFWKUsAnQxUUkClEE/8EWdlUngYWihf+kQkW87/OA2E4Ol7CVlcVELOlv6qlpbUwo5fzoIl0RZDLkGwSjGTi3BSxdJ+fXMgIOcTPCuOsLDwCgrO8vBsPFj5FZa6FjqmUnkJ+0QpQdasK54mRR2UczpZfqck6Fpa6o2FxKhSgdkhBXWajJb7xNgaV93ns0RTCtdi0JbLfbUocZZIeXLGT6KEsk5kwsbZUosWYmTi1L4MfkZpja1ccZu0J/mxNkUyejop994P8hJ1crydRlkMhODJUQqtEyRvJeekW7VC/s3lfvdeWoxFfadObVr5QKMVTSUcKbEoj6L+MeXm16XvsexgKhNyKu+5qC/Kc6mUKmRlVXxkFkmuOp3D5WFYPHBOix6zXGvBtWIhdMtEWZx3C39BZ3k+tMrE0npb+BPWuOKKvHiuGEKCYbovSMfjnjCPgsoVedJpzxZAcWnLlLtGG3W6l++f628Oqy9DysI5yZ/mn3D/q8spWcY9paBbG4wVqXwMieNB3IAfClW++awiqe44C9bSrjdYJ4GBIQR2u1usNaxWnRT4BcVKGY7DSIiRnDJWa5pKPHcqZ0+F93L+pbWT8TGcuFsnBJZlXHwwPp0OtLTDymvFC2lpi1MM/B6clPJPpRTO2ULUN6fvLWPdguSTkXDUbnU6wTrcocIe54pix4jUfncIWKNoa0NdSbGM4jSWKyWy/IXwvVyjhVclid6JeRC0OCaYPIxzZgrgo8KYDqUdTSX5UD5pIlJ06rw8ObJpPNYE1u1RrmO2jHPNNNdSDLOMuarcoDz4bWirRFUck6dp4m//7b/N3d0d2+32m96jy/YdgaD0/UDfD//1F36HbdJWSsxh+u/+WdqKFPbhppTBVPK9mDPRz6In/E1sVdO+8f9lKEkpfdPXBzR7JEbgzd35Vumay85+65d+s7FUfervh/tWQJrT9/xpp3+dz/9UvUQ+cacx1Tf7hTc3E40E2H3LvT6VxdR2df+tUL7K0dzvhpyE2pYPP73u/iB+PZB2+f6yS9J1tlRaTApx3+SX5kji/iAUihwpRNRv3GLKHMdTjjVoJ4v55QXf6nwXKsEbO79csPhNXv8NR7X8/eAmC+X742/k/n74wQa4v8fjN3z+Nx5Es9rAavMb+Jz/Mdsbt1QFTffrvPg3uFlrubx8/Mb3Sv2FRtK8v9k2JwQhevigllvwW6e3/Qb36dv4nTeuZtkn131rA9Jkz8Ce3d+W5R92LX8PIMF84Rt/9ze9GTAtfKvLJce7DBrf/BUxnp3+V1mo7MPX/roP17e1fXrY/O3tt7ff3n57++3tt7ff3n57+x++fUcgKKoeUd1BPBvIzF4UD2lpWSxmMkoVi3p9b75TYMEUorQYKifwbvEe4QG8K+0SVYzDpLUwTZOEu5U+o1paCbHA4FqslKWyFqKodU4Ik34mp3jPByG/mTqvlHAqEA6LDmvMLCsrBbS1BGTFAkV7H4q7ayPEsRRPvVmlJbq7aRqMFmMfIRUvbaUFzi6Kl7ILC9FrgeGtEVjfFvOkEII4Mc73/BiBO3WBndX98ZRWm3rQIlvaJYth18moK1MIm4sDb9kfpfHmDfro/VILThbucvoeMhoeNK5Ox1fcVB9Atg87ngso/OA/b/4sf/o/4oMQQxDOSCpk7LyYXeU33+sEjd632VBynaqqpn2watzfXTPPY0HN7mPbJQxN2pDWKIwGZ0WyqHXJIon3xykedw9akuHBgRVEZ0m1Wq5LCPk+FG05r0ubr6geTi3SzKndaMwSqCivzwhkvphxyb0uzsZyehRte44pkNIcPNf7HfenbjnmT1+Le/h+aVUurdT7c6tKm49v8h7FebUc/wntz8vVf/Dict4lBJJTG0J2v5zX9HCflvd/+Bw8fLtMZSs23fr0vcePH7Ne3///N7vlQv6Yxl6iDaIXd+DSCokxCBesaXFNi6s7lDKg9APk8B5FGqeJcRwYx5F5nhkH8RA5Hg4AVM6xWq85P79gvV7TtF051m/eYlr28dWrV/SlZS0k/GvJuFqt2G7PODs7l2Rho+j3O/w8srt5fR/cV1oXubTYlvbdqX1SKB9W31/ch0aAy36klEU1UzZjvvF6LW2wtLTDTtc3l1bnPYUg5UwiM86JOSQmn0QNhmQOPXny5PQMan9AhV6UolrhKlfaQ+pko59LO3bpLikl5pehOELHMj5bo5eMxKXfc/rdN2kNpsxhS9tdnmMf08kfRxfl6nIORKyxtLDkuU6FNL9QCrquKa3Vez6mmHtahmCZ038F5f4m23dEgaIffYL9nv+FwETMMzcvZ4YhMSVTIPSMNVBZxaqr6ZqKxjqxHp5F1THseqqu4/HTJ4zTRD+NpHkkh8C62+JMRV2tMNbRVCuUrlCm5usffcTrmxtWXYU1GhtlIPDjgDYK4zRDCBLaZzowFavLJ8QU2b/+kGka6IcdqIhSkmpZTGqFaKs7mSTiRHf9u1i9/CFACo73nq7passwzwzjxKurI5fnW95++31u7+44Ho8cppGYEqaqWXUrPvf+Z2nblu12wzgNTPOEc3JTTcORGD3zNFDKLPb7if1hpm0l4vtsXZW/t8yz5/b2jlevr3nx4lVpMiguH22o64qmc2ircE6Xfn4u9v/69FBMYxKTrZiwzlB1jsWH+/WrK/rDwN3ddLJAn2zNq/ZSzoGizAaZwoQtRGbphkofWxcvB7EBz1leIxN8knNsxBNDPnZ5mEvLJC/7unCJihNvluINgLxQGSP9buR4d00a9qR5ZLq7IvsZHYWop088pEzWGVQmFcVW0mCNZbXqePr0Xb7ru35nefvEF//Tv+Pli6/jKQqD4IV7oDSNkdTby41lVWsenVnqStE0mtnD3SEJqdUZXGMwlSKVQWTY+ft2k8kkCyOZuRT6IWTubj0+ZPxUSJXW4qwMiGmaxX9nDOQEOimsNdRtTddWtG1F3Tis0/jomUPk1c0OVMY1CucMrnaEkCEbvvvz/zPr1RMArvc7/m//31+QySOV60UuEwjFFVk4AkvRdPL2SfFUEC0HuAzmqhCjZVNo5YQL4zLagHOJFCB6ZFFS+EjyJQ6uTQ44o2mcwRtN1JqkDUlp/HzPidAUh2NjsKYMt1kmNuEABZ5dPOX3fOEHT5PW7//9v58f+IEf+LbHw5wi0c98+LVf4cVHv8Z0eE2cevzhSPAzx8OO1dk5zz/zlCfvfw9P3vsCyq7RumKh96gHHJYPPvqIL//aV/jqV7/Mq1cv+OijF1xdveaX/sMvolA8ffKY3/U//SC/+we+n//pB/8PfO67vrtwk379AuUf/+N/zBe/+EUAjsc9/+b/+f9gsz3jC9/7O/nCF76XH/7h/xOPn1zSNI5f+cV/w6uPvsq/+7//AsNhh++PDB6OkyL4gRQ9q64qHDnhgzibaZ1h1Vi01ShtMMWteGmPRx+ZRk+/G0/8FNc6nNMlD42SzSN8vBgSk8+SzZRLbpFWVNZJlzcGppLi/WsvB17cTnz4eqCfRNb09ttv8X/9o/+XU7Ky3X0Zd/dF1uuaunY87i4KQdgwzzOH/kiYJ6KfsFqKeesqUoKbfc80B479QN3WbFcNtQFbEr2XsS7GKOKNwl3ruhV13aK1+IWFmJh85OpuxPvIOEWqpqZuKlHEkrg7DGQ0TbfGObHGGMeRYRyoa0ddOX7Hs8+yWrVYA2M/8vLFa+p2Rbc940u7Cz4Zvnmb7tfbviMKlFhSWbXLaCuGSzFljncTISWYAm3jaDct3ieOaYQ6UFlNowxo+X3xIKlQSgh50zwTvaftPMYoZo6S7pkCOlt0cig9UFURZRJZZ1L2pBzwahbSmIcpRHxMNK7DOrFej0lcDXOGVbdB3JgCWc9AIOlEypGp74V1byUw7OFmtJKbVskEs8SN5ywkWB/8aaUtBlNC2FxW0/dhV7IaXFYJMvlG5nnmkxev+PCjVzx6dCGpzfUzYdFHCD5xHGaurnd8/YOPxR1Uaaq6RmmDSw6V7q31Qd47J5ncU0onUqSsTATVSkkGAqUUxi6roge8AGU5kT4WqUAIpBiY+8NpheWsKImcESOuBTFL8/CA/GjQVQUPiL0yPJfBaZGQL6vkvGToQFKaxedmIahFSjBhpqxGktiiRy+BWumE9dyHR+qFwCtdXD/PD0jMy2lLZCWOpUaJNT0ZVJY8FANC5NWiArEaGpVwGpTNYJKgMyhRB1hN0opcJUHJUiSkzDwn+pQYU2KepEAZ50hOFJ8FcFXCuYw1YqCVdSaKZxhWCYFx29U0XUvTtaI+oKjjfIKoiFncn9EB9FzuHUt4/+E9vqhYROKsixnIUj7GEEvhuxQoIodVaXHsXM40ct1TJuaESg+4AlmJ4iNrtBUzrynNovTyoHU6KbIUiM9ILhOUBQqpW2ld/G7EWyWlLAm/BWU5GZncfzBkUaEIgnq/LeaN6sGq/FshEvnhc4F8TCSJbD5OhGlPGG4J45EwHAh+Jgx3pEZB7NF5xpqEtiKpfuBtvWC6qELA1eXeMkq8k1Qp8oOfCX4i+AlyxOh7I8xvVaQs4ZAPDkTUODmjtDmFW1pjMEqTgicWxFkSf4saMgmHScaTKEF6aCGCn/xdVFlEFLQ1FyJTyvK+Zew4EWcz6EIUBRmrchTiqQ+J2Sfh+RrZcZVBFTc8pUTNtzjfTsGXOBApUOKnuHUybsvfMsxlKTBn8ZaK80COAclVkoVSCIGQMuM0EUJAZY9VhtokTAqonEopL0j+Ivo4lZzLmJmliM8poUqxFdXyM/meuLPD3f4oJHg/oxA1XfAyP0YtSieJyAjYymENJXSyvN+nIejf4PYdUaCEAHMP7cZS15auFafXly9eMo4irVVnFY/WW6bjgYM/kjeaptLU3QoyzCM0lcGpFSrOxDnTHwPTOLPZRjSBOY6oqBiCw6rFjibRNJwyMAKekANjFhfWOSwPT6btDM7W6GwhRoaDp2lqLs8vyQQynnG+xceBgLQKdrdHjMmsNrIqXDYFYrCmZTBZzKYUAuHO88QwjsXLQVoyRmsxKYpRzOiCZ54nYpDCRhQ+InUex4n9bs+XfvVL/OIv/mc+89n3efL0MU8vz2lcwzRF+t7z+ubIVz/4hF/65f9CCOJOe375CG1rXFOTKfkWcGpPqCjy6RgS4zCf2jdaC7vRz4FpFOKvPalKlq0Y5RHRSRJVSbHIOSeG29dEP+PniaauUd0K17YntUBOmXTck1IUBYxzkDox0bOLDHRpSSjRTygl5Mx0D3zn/GCuUSD+LRnJQ7kfmE+tnSS5RicUZpl4TwdPkSJn/CwBjW9sBpRTNK0Tq/zK3Cc6R5GKCzqicZWlcoq1TSiVOWsLvqM0WRtQBuUqkpKE0ylEdtPM7COHObOfIv0cC7oFJLk2TW2oak23Eqt+YxLRapJXpGxQUdFgWbcNzx5tqVYbXLdh1w/0xYhwmiMGh/dwt5+ZQ2QKkawyxtoi9y+ntahmckYCCnUubSM5vb4oy+4Hv1SKYXVq1UgxXl6hH5Du8j0itozcS0zGNAZRBCUtc5kRczzFYjchBYMx4hyqjFjKxyDKicUDIifJuEFlclKClL0xVr+ZmL1sC7RvTMlpLveNUveohvx2+ZyleCm5MClGQpgY+zuOu1dMty+I4540HIjzzHjcUbtAHJ+Q/R4VR7AdCndq490jigqfIz6JiaC4o2pqY6iKt1KKHj+LJ5KfS0sJVWCq+31++Bx/Wjy64GNog3aS9+WcGFvGEJiGnqk/QhRl0+ItkpBsKJ90QaxOZVVxrJd2i1rOVV6qU3VaIIUQi+fKkruTShZ2aaUUR+HJS9r9FBJN7WgqjffxVEAuppo5JXzKzCkzR9lHSW5PDxrN5bE30vJXxoIyxCRI3zj0zPPMPA6SB3RCfcSheg6R4zCSs7gxOw2thTgFog/EtADRhpgVEXu/MFVaCpgkz0yJJ8IaQ9S5FEuyz20ni2p1dUsKnnmeT2pT7z0hlMIOXUI3K4ypS9RKmVfiN7GL+A1u3xkFSkr0c4Axl0mjxjnD5faCuRY/lEbXhKFmHifG2WKJxDqycYra1vz/yPuTENvWLa8b/o2nmHOuIiJ2cap7stB8JS1IBHuC2hRsiGhTsGHRULQhIigK1iiiDUm0IWhLsQBbNu3YE0WRT/34sEwz82bmveecfXYREauYcz7l2xjPnGvFPifz3nPfhE/uO++Ns/eOWLHWLJ5ijP/4j///x37kY3abG+42H/D4LnB4/Y63X6qHxMvnugCF2hRgw4nOGgbnQNT/J+agfIqUyKmQo1UlyFibxJLKVMPIPCfCHEkxUz0IvnE/QLA447GywQG321tKicTpSIlPOc1Lhg5oHbT12OeSCVEHk/UeK0a1MkSYw9xEhLaAisXVmpt+Rm1Qpk7ceQyMp5nTYeSXfvG7vHv3wO1+y/O7O3abG47jzC998SW/9J0veP3ukc0wsB06nB9wfqBWISeYa17b4xZuTwhBA5RJSxV931FS0cxhDsQQVc+md4hctVZfZcoGqClS0kw8PpDCRD7ea6Y+n5G5x4YzPt3ghwFnLEKlr3NDbQomFZgq1XdI9hg/QNOrKCIXXvoVV+Uy1+qKtMDCeZCFXKGQ8RKEtFKUiPtqLnG9ZhV1kC7vIShqIFewNQMGZ7Qt0XaWVVmusyRnOIkhA64KnQiDB4vgqra9llSIBwgZvjycGVPmEBNTLoypcJ4zU8qEWcek9xbnBCMVawrWgncG7y2nUVGWPGdM1gXJd4m9ZGoKlOnMfBqZ5sB0nlTXJwdqzNCk6yUrN8xdcAq9o1Vh9cV2wndOpb+XxpqUKEV007l+JrVetYTq3V5a1Zc2Ud3A2rORZhSntRec1SAV0VZrY4zyqgSYg5YMBVItjCGQraMYo8lI40WsMBu6T4tpgVXzutESFKu53Tc76tf8fSEdLN9TgUGkoA4bBoojm0qfPF3f0XedJgBNRUWaXk8biKu3yqIhknMhpqxBZS7EXMgpM82ROaiomW6MyrW7cM2eBidfd4gIXecVNXFOtZaM6vTnXJjGA9N4IKfQEIV29UqwU/THtrJjC1NyVdHFlVyEJgCVrBzF0rSBStHn3fQhF9PD0uwAUtTrnuZMpmqga0QNPUtdn+OKkkUhlUpMRT12FkXbFfu7HM57+s2ga2OF8zgh1FbWCZQcm6qxXX1u5pQJMZNywgp0ncGYSlnMaAXEO6wYnOuoVbArgUzvV25BBlWagW2FsrTLL3uK0UCx92x3e5wLirY/4fGIKrbnwps3bwnziHAHtbLdbRn2d+zuXvLZZx1M33Ngf+X4oQhQcq5MISFW54Nr5Y7b3Z7owZWAGEuaHHFyhNkyGR2Y8QY23vPxh58wdHt2/R3Etxzfwv2ryniG+SeEzguhJuYQORwnNt5Rho5uEFynEGRSRiI5Q8mGklSnojbTFXUJngnziRiVl1IKmqkVjfhVLM1hzJYiltJbQjhzOI+UdOXFQ9NVKa0HvkW9FS3PpJSIMerEsSrgJECIgS55Um4S+da2MktpJZ9GjI2ZGBJhCkzjzDjPuLf3vHxxx9vbW/bbO85z4Luv3/LFl295PJzxrkeMw1qvE6O0jTWrZ456Dum5hTmSc2aeY9sAvAqZBfUkyjHhN73C5+8tbpflFHKO1DCRzkfiPJLHIznOpOmEiWqdnk2lSAKr3h2+RFbJ6lwabKyLsBinpRDjkKoNd0uFajmNuuwDy6bQ/l5bgNLobOuTEnTyX3+7Xu8ryx+1lS9KXqW71zHeMDZXq5aKUP0D7xXOrqVSnSFbw9gCK1+VazI4wdSK1cEBWSizegzdv5s4p8Jj1qxvLjqXYsyEoGRiI6ZB5w05EHVW9W7ZQCDFis2qUeJKYSOFOUfSLMRpYp5m4qSQtCWpNwuCrYriqAz6haSp96iuxp0VJe65zuGs3nc3axKgku1XisHLDV3f7EISlNbbLWKaOM3y+koVhesXrR/EIFZW0UUrUOdl9mmZNseowa5RPZNFZ6bWRVtFUU5jlizYNIFBfb5KDP9qgPJ1G7qiKE++c/WHntOCVcoimCIFY1V5VIrFSMF3nm7x6rJOeTbV6Nc6OLUsZdq8qFWJnjFpYBKT/jsVFRYMMRFSVkuKRatnQbLeu6avk95a0DINTpwivta00lwiTGfCfKLkpqx7mTAacLYARbkXsq5lX/moRpAvqXn75NKCV3XkXozSpdb1PTRh0yClGllFCnVNa15DtrU6i1AlkbIKKab2OdrAwVdOSIUr++aRVlvClpEUyCkq2R5WF3otlWr5KJeMsYJzGjyXmpuvM6pnZB2u66kYTF3K6C34qkvsvny/rsFJ1UxZOV3WYX3HZqN+atM4N9L7kiwKOSZSTTw+PpLTzGaw9F3HMPTs93vunj+ne9tqwN/w+KEIUKYQeP3uge5QcbZyswt0zuNl0EUlRWISDlEQX/DOYs2AIExng6+V8iwRw8y704Fv/493/Od/8yVvvjwS5sSPfvoRRIPdD5ja05kd1njE9BzPB+Lxnr7zWOPY+FsKFpstYgUzGE7hkSmdNON1lcE5ZCqEVxP3ITGdIs5lrC8Mu4LzleM5UIrHygukeDw7DFeCGRVCKtiYmKNG1DFXpjliT2emEIg5YWOCKszTTK2qO5JSZg5zG4wXG/Oalc0+nmdCyBjj2WxvePb8Je8eHzieZv77//w5hr5nt72hIISi9us3N7e8ePGCF8+fsel7rECOSRe2qnC1c8J4nhvUH5Rr0hjoBoizIU5TW1gu5ZDrYy28lIikQDg9Ek6PHN9+oQHKdICcsHHGSVGJ6Dxjo8VRFC6tSzBWVQiKglQHxRDnMzU6/FaF46KosFhu2f21jF5LQPR90NozawbSMpZ2xqbB3W3baEhfg+5bfXkp81x/zuV5WyiOea7EZjZpjWGyuuEpwU/r+idncFYYO8PGCVMvdAIdUFKlpqzuwKEyz+orMoilk8pAxWclArqkC9cmgqfSTeBKRTLUcyG7TH+o+AC9c3gHd51jcMI4RkYKowREEptN5cdut3hneHnXU6uocNQ5MB5VkbhU2PVP1UVz21DUFTsxzhm3KC0336IVQZGFh7V08dR1+Fhn1vdcM2qrG5kxTdCxfVatqkhsmuAjAilFCkuNX8dNppKqCkIqrUFLAquWhSjR0nuzcgy0tNgCFFGI//1H/b2O9wOVNUxeg15ZUQVjHNU6KE6NM2sF58F32G6DuB7Esxa/1hqmjkUjYKoGbmlBT2Ih5EI1jkzhOEVOc2KOlVSk8bZkPbfv62iRnDSZetO6WkATiGVDtF5RDfXW0U8w7eY6Z3FeSGrETUiFmKWR85WwvCC4NTXkJBX1PSrqum6ktg5QLglbqbq2xkLXOwZvsK207qzWG43zGjzUBFhKNVSxVOMQibCUzOp7GIpmiQ3ZqUibzw0zwUpVTmPKzWzW0HmjdgllUA6Lt4izYAxuqwhwN2xBjDYNNJNHK5r8rn0FdUG4VLiy5EQKgXkacV1PGRRtsbbjo48+IsbI48PjmgArAu+I4dJtOYfIHGaMgU1zJ+/6AWNH/l8boOTWYltywUpl6CasJAari5CRBBVygs6ru+QSbadkiFFbWnM2pLPl7Zcjr75z5v7NTIqF8QDhztAP2t5qxGCkA+mI+cgUA96pp4c3HVhPNb65W1pymUl5asJczWDQaMSbQiFMj/SD0A3Qb5RHH9pD76zClEY812aBy3Wn3P4stU2k5uraWgqXCVkWd+aqypc556u6bF27bHLWe6ndDioJPWx2cDgQc+H+4YDzI+OUVSq86wFhGAa22y277RZrBEpphMFGDGuTTjPz2LyT2jmaQoyGkgWKwsMsBNTyPijaNvSSqTmSw0SczoTxRJpHSDO1ZEwtWApOVH3WUjC1tcS16y4UhaMbYUwhH1WflKpmjUv7oDaK6yJu0Ale2s4g0rJMtJSztJrDFdqykG91R9WfLRd0SVjb9X0lPGmQuda3C42Uem3mVlk/syTlCkgWolpz0EmllwoxU1PhPCXmWKAoGtUvSsOiJUldxRR29llJtyY2rUgRyBWxlS4LUoW9F7wx3HQW49QwUamMBXEV72Cz9wy95YMPthjR5z2dZs4bwzwKKVY6+xRNqEuGLDpuU9ILXjfoqz+F5bXylSxd24EvGAOg9iHt+S3loPUZyOVNBRTVqgXbWM6KYAqpLsF0uSolNPXdqzZqWd5nCYwX8OOrYML3PJZrv1CzL0HKanffSqEXNV2hSGuJFqPBgGlO6VflSz2pq8+q7R6tiMKCRjUuQ20GrFnRFB3rP5i8lqpJmzU4UWCproRa9TbT+1nkctoLNccsaJToXdGS9XuifQ0RuuzSyx+yJg8LAtqqHms7cyoa5DtzCThp/AsrSpKWuphMChfbRtYx+cs+brkgewtSKUJrXrgE4SoXIUiRteyoLcoWMboeG9fh+gFEiClRJevYNlrqL9JkA1pedAkm236wID4NSTPNSLPrOsKsXmoLR0oVrKGIdjUtrcca+Lexb5+Wbr/J8UMRoPRdz7Nnz+hsxdvCbjvT+8TNtlKzoXMdOTri7DFeME4wVv1hymSYHuBn3n1JPAvHN47/8f99xWc/f6YkwVnPfNgwHbZU3+C9bqAaQzSG6juM8RhVb2bfb/F1i+lvtNnEF8xhhvPMXGbmaWrErMJu33M+R97dH9i7DkvHdrdnv/ccD++Y58D929c4L2xvr3YwdAJOSaXVYynMqShvoAZCkUY8VE0N5Z88zWZ0Amu3S05KFJvOY+u0SE2yX+g3W+5evODN4yMyBbJYzS5dh+s6NrtBSwBiePn8Gc9ub4jTmRNR1ykxFLHkpAFijFoCy408nHLCSKWmSOcc0jXSoXVM06zZWLlct9SMKTM5HEnnRw73X3C6f0M4vqPEQIkTtrX9bfueD549Y7fZKmF2zTDbwmSU1xFyhqIS7ba5U5s0gvP0Tk3ldaFWLYDc7t8l1Gl1bTKxbcvSdp5Ua/MaMsvWta6qpjlMLzV+c7VQvD+du86w2ViwFmNgGLS7wVuDxWDFkkMmp8p0DoRUOMbG0pemG5ETthRshTvnGKzhk1u3doJpcFs4OsvYCcFYLWNkJSObDMYZbDD0vWHwhpd3WzadZ9dpCYSqPIyQ1QW8t4bgKsmCdFBc5RhnBue46TxbET7oHCX0lCy87Q2LVquIlk9VfbXqBrtsWHVF1MHZFdeqpZKaJYVwsX3AXHRYlgCnVYqwbdHfdh2Clk5TVp+cBZooKWNKbt0aSp7OFWIDy2pR/xwxTUeDFoxIIVdR/pO15GkmlURtkrglaub5zY524hVoo2bxiy1wKUlW5SHRuCI5zOS8dAMWxGgJqBlb6ca+rBP16afpJ+k92ww909BrQlM0gbreeKW99ptsSSKC7zv6oWe7UaNH5HI9frOj390wbHYEPWNsLIhR/txCxJR2jlmWpvT3NuHWhi7GkHNFcmwBThsfsCI5VKGi60MohYggzjIMDufauFosC5zTQVAiMWZO55nzFJiCEobFGMjlq/ekZihK6DfGKvesKCpiTIHGzDIiGNdpKUtUgySXpDIDfcdus+F2u6E434jw0lANnfeV0iQCBFMMtaDrfFE3Zuvg5maLdY5UlBuj3JSFQKvaVpvtFs6oO7h12F7LhDlnrMSmxaIoWDcMVAPjfCSVb46ewA9JgEKFmjXw8FbY9J6+rzi/kBxbVBi1DdYaR62WXC3zUaihcjpF5mPl4dXMw5tAHAEEK8I8wXwGu/dYcXgZtDPFFqQ0my6p1JrJKWDE41wFW6k2I65inFCDIhk5qWaE77yawjkwTnRQWkUP1NQO5imSi9AXS6lPF7KUEkIjZLW6sKmZKhc/E2ONokUtm1oj45ZlrOjJWqOEVTpaxUS0PNHg7qVLQied1j+dMTjr1D3WGlKKxFCxTlu4jXMtm4lrR9OSabKSF3USL8GJukC37PQ66y2FEidiOBOmI2E6E8NITgFqovMO7xzb7bb1+6urszWmpVJcFmLRjdlWbT+tJbVMDKTEVsL3SOMNrLBortfpqqI8tRn0tU1sRU0aGlRaOLPUfZclfc2G5QKJL1/XhzGq0WGcboCdV8NGbw1OLFYsxRhKKkgqBDLTWc0pQzOHTDnhq076zWDwXuioytlquE2VhqY43fiUd9CeUVbcv0YN7goVg1VxwM1G7eVBEbhxRlxrE7fawhib/9B5VvdqSRVfMn4h7Nn3rrplxYUFipY1013+tdzJ5Tev7uzlTa7uZpXL5qlBu+BMC1La5mxN695YobIllb580lKme8I3WrLedh8WtCHnpXvjMv9WhI2voj3f63i/RVeWeyPX79PKfm18spQ2Vt7N9/jM9wdgvVypahnZJ2jQ8rWgLZff+5r3+rqPE1FzVefovG+iazTuS16vTdp6VI3VlmdTVvTo/SSMJytHuyfSXt26ap6+GlbftvZmevvqioQqYiEXZKqtI8tnL4hxSJmUC2nRPfrljqqlYSWgszpKr3w3hfjWIFtsE4UT3ZuMEaxoUIDYNp5QBDuXdd68P0aXKHTxFxPRce+8xXvfvNvKk/2hVuWBLb5u2p9kWnXuIsqoKJjFOg8i5JJ+IKQQfkgClOkceRtOfPiiY3vr+fDZc3Y7x/1hIsTM4+PMeA+Hz07c3d1yc9NzjkJIlYdXlXAujF9mzg+R19898fAQyHOPuEzK8PrLGWzHJ9s7Bjp2t3ukG5HNQUXOsqXEmTkmXh2+Q+c2PL+Lqu4aIiMTpcvksZBCIQdlfd/c7ZHec6qZ7U3Pdj+QgWlO5OwpyXA4BlxfMTsY0pWbcYXzNGKM1n1DzJyngLUVnzWXMkaZ8X3X0/UdxtrV9M+0OrGSWNW6XJrhlSuacZUYKUDMsS1Eoq22RbNMAYbOq96Ic1o6E5jGkRQMw3aD855u8MzzxPF0WhfjxbgtZ4Uoh50GE7vtdg2GUsnNqOuy7OQ0MYaR08MbzvevOT2+JoxHSCe8tXzr0x9lv7/hww8+0sChlObzCqtuitF80xhV/jXecZ5nwjxifI+IRZIaYuWSyRhKtWuX07LGl6aJUpuDcE6BEkak1cyrMdhuQ8UQp7OWpYrqCCxKpMu+vCxwv9xaLqZgbKLrlMuz3xu8s2x6jzPa8eCKwVSYDz3TKfDthzecQ+R8nEkFUqkrsc+ajKNio8EWVS4zRTUuBu+wrtI1EuQ4aR2/5EJNlVgyNWWiSww7yM6z/+gT7GZgv9sxnx5Jn/8S1mWMK8wGohTe5cIUC5/dj2oimCobZ9h2lk1n1Wn2yihEpHUp5RaYVrPqsYhALWktYwKXVkz9bR0vut6/V3XQDWBxqe9audcvhoKLCjKihO2ckKS6EOI0cE61khcMXi4mncYIvvFoUlSOVRhnFYU05sJzQZEYqV+Hl/3Kx1eDk/VfXAceCxJSclEJgaRl3iuW99XxPXaQqx8vAUpKaU2SLp1UX/M+30eQokjAhu12y+3NXh20BVKMhDAyhxNzPEOtWGOwXUefIpuuMDUhvyXBKSFe2rfberMQlZfdOZJWEr82KhSKFJJpbdsNcavoGCpcgpPOanAgtPKFiIoc5kKkMqXCcU6Ms5ayWcjb70NNoGhWiRgchoI3SocvKZNKZowB8V7HloXaSLxGwFtRdKVCjoVxjGQJeq5N4dlhKALpiav15T2qqTjTDCfF0FfHdrdhSpUQAjFGYkwrsibWNnPbqBzDHKhNIMdawXcO3/V0/UC32VKdI+R8cZP/hscPRYBSshCSMJ1hspV51MwyBUOOhRyE8Rh58yowHjyPvSMxkKvj+CCkSZjvLeMhM52ENFuofiUXPb4964I2WHa3G5zbYFPBGRoHRHDi9QE2Kdg5BjKZuQQiSZcjZ7FFu3lqFVJDE9aA3VzqqPo9RYGsXTLI6y6e1jKaMyGJqn7GDFicu/TlO2fxftESUSffUi7cEOWxJA1QjME4i+97qgRKjIQUGaezCoct0HoVvLMYEVIIUApyVXtcZoDWjC0itfEH4rq4LvoVxiiB0XmP6zpsP7RJYDA56u9ctbXlnJjGM/P5yDSeyDlAY/U7Z7i5veXm5pbbu2ekEJmnWZ9LVX2I5RpAM91aClKWok0l54DBYodOUaKSqFXb8kpemP9Lxthg4bygZ5GaE6bxEaq1dP2gpD+UhFYy+nk1wVU2Xa6y4iey+O1Y26uNWbNLWpkua70KJxZjhM0g+Or46NnAebR0LQiNqap+j4HeGJyohoQgdFaVZjtjiWLJWELjNXV9IKfMPIUm3591Y82QQybOiRC0nTXiiKYnui2JgMmBWAq5oXU1VxX5S5UQMjlXUoE5VTon3NxWnL9csy6i6OTIWXk/tIx24UO0Teiit7Bk+xcJchUprEhRorSTprgJWKnNGLkVStoYtxh1Kl4/Z8k5WeeXsQsecyklLRn34mC87E1aypP1/AW+0Zr9lc6ep/HIV167/Gi17uCyYQMr7FGvApavnM4yJuX61xY+SlmTnaUUdvnvNzvqEg6s5yGtdKYlijRPpHnSrr2SFT2QRahSWPhSl6DkmsexbMCs2kOlcYouXJflXl1Q2+V/pc1z26wkVPdDL/RavZhyUcWeo45ttYJovKX63v1nGeONDEXFmBYUrXdSmiNzoSZ1jabS9J+080wVraO+tbMXd2NZxr9Zn18pmZob0bguhcEFPdLxbYxBVgdtVWwStCRtO0fuO4Zhw+ou3nhMzhqcc/Rdh/c91nfgeorrERPXq/omxw9FgJKiME6WA0DMPDyDEgwhOeIIcQw8vk38/M88aCaUE/3mJc47cjbUVCkHx3wujEdPDMoLkFypRL749lvuvzxweJh49uIWk/cMzxObpETAIoZu32OcpdttkSKcppmQAmMakd4incF0Hc5CmCet7Y+Nnd2WPSO1wZxGM2ZX2WycEsMqSH1KPkslUmviPGq/fQiaKnZdXb1Y+r5TjZHaFqoUW0CSiM1HZwqBlDOb7QZrHbbvqWfD4XRmnEbuH94R5xlpwmgWYdv3GCOcjyecs0TvuNltqZuhtRpr+5xxqg9QSiLGmaU1bQlmus7ivaXfDPSbLX63x3mPcZaSZlIMGPP2cs1h5vjuFefHe86He0yZMCSMFbq+48OPPuL29hkffPAtTscjD+/u19mXY9Jgirbg2krNmRxbC3BtSolJcDcbMIaxRMgqe56SZsQl1ZV4d01EriU1xEbZ+BjB7W8ppRC6gZIDebaUFMhxoqSoSpbLObXzMvCEdwMoea55KBl7IYKGGNfN2HYO6yy3W48dPM/lGdMpcD8YwpyZ57Ru7FYUiZhat8F28PR9zzAM1G5HsT2havlwHs/EOXC4f2AeZ06HE1I00AvjjMFwOs5kPLKzhDpw6l9AOEAolKpt3U0XCpN0zp3nzBgKR1NUndRVfuKDui5KwtKVc0HAaq1rwFByXoOTJy3GXNoga9UNxVgLVSFpb1TYqhOwC8zNEnxrWcFg8OgGWUsiL5syspYHbSNtNmOF1moqxKxBTYl53ZCWvUmWK6u6Kb8fc3zd8ctpiKwBTv3l36S2uZZzbp0iyw+uajLr995796UMINcvWNSnL/o0K9rw1f33G13T4g1GG6Oq9JrIMRCOj8zHB+I8IqXgXIczht6oOjJV275TvpRUtPQha3BihMaZKeSs6sXeaXu4kRaIlKWkcwkqYtX72DnwreNwiaU0CTPaSVO0+22cC8cpE5qH1drv/DVBihHVvVKOW8E6/azYglkjRhGclKmpApGQs7bDo517pVRiSjAH+u0GbzolzS73ura5V7VdOsVCybWJYJp1LctN6HAhtxqpqsxcMwaPs4btZmgdms+ZxpFxPK9SEL23DF3Hdrtju93S9VvE76Db49w9cPr+BsfV8UMRoDjn2G53DEOl6yrWKXG1lhlrhGfPd6Sz51s/6nj3meH+FaQZrAPvG8yaDVTToEDFhQWQIspHyYV3jEyPlTx/h+F5YfdhotvN+EEoz9VzJG83UItC+pLBKUxZaqHzvYr7eEhT5uHdURcp2+p/1rYssKgYkAXndeLnoFop14f22OcrXocO/lLyWk/XzpzMaZwRY+g2G6hVsw+nWrgqIIdO8lxIKfH4cOA73/kub9685XQ6t7ayy4asRlDSatpNxVYq1sF+v2EYOjZ77fBJpYl7edU7cc41BjhQM9apLoOzisosGY4R08TVLtdtqHgqvTPUziHZIwVMUV2UN69fE0PG+y1SYbfbt9bOQpCZmou2BkpFJBOicneGweM70xAtYei6poSpAly2JmItpFoIUptJmYaWRVRxdAkzKlzxAsAUg/QDtTiKE+0+ShtyUkG2EiOLJH4thVwi+T2jcd8JwyD0neAsOFMuzRdZoKhYWgiFCSXCEkRF1DBUUzHOshQUvNGNJ5Wqm6+xuM2G/YuXDC8+xu+f83BSwnQ4HUjzSOc7xsMRVwomJ6RkfA6YuTI/vKXGGawnIcRiydmTY0+YZ3KqzDmRKRRrMNXQOb9aAuRaMfnCnWoDcs3eWuOJdt7ltOrEXLQ1eIJGLhj2YkhIKSpq5ewaoJiSNAvNFyRLWga5sFukqPfOwgWwjUDZdQasRZwjxkrKCxm65d21XrrNK5rpVhrhkSbO1rLv7ydK4ZcJVK7KJ1/F3fS7q6CjsBJCvz4yegqjXBWS0KBPO8nyQr5cEBRj18/9fgOur+PdLKjkajmxEjGqzpOkFiSmXYNysUzzhdKxssBK191aLPw2YxqKRUMJAEwjoy5dj0uwW1rnjrYYI0LftwTB2EZnkzVAWZCKnCEXQ64OYytdJ5SswXEtqnvylRJdQ3dLKYQ5KMorSrzdGMscIlPbQ2oF16KjggKLtilgK29PgxWR0lBbbZbAN6SvnbcO50VMb1kPLdaCc0JXpb2HdjamMFKzIzldj4dhs/JTbJtnZNVuCTHgU6ZiMMZh3cD7Hajf7/HDEaB4x9Zt6PtM1xWs7bT/v9XUnz3fUMPA6Uc2nN5OjKeJaLR1crc3ys3LSkzVEktVhnsBiiGNkOfKdBo5+Mjj28DmGew/hLsPPLs7i0TPZtvD3QAk5vMjri/0ew1QYo70g8M6QWwl18S7d494b7m57ZsYliXlqj1t0gIUV3XQh9bWenXknMk5kVJdF/bFjVgFYJqcc8qczmeMMbi+1wkqgjT5ex/CCvGVXJimmYfHRz777HPevNUAZSFh15ZBxagCa7XxGkqTVbdW2O8HttsN3dCRS+E0ThhT8d6oeVzfM2wGjDGEcUSMbcS4FqBQqGV5FvbJomcAL5VqBeMd1WRteS3q+/z2zRtSLOx2z9hut9zsb0gxrmqO2eRW8qpAoLSJOAweaweWxdg3glcnEEzBlsJclc0vAhEak6S1b4J62yyZZ5Mdr23+W2OhZqQ6XWxLUh+LnIjzrN5H06RuyFk7P64P74ShMwzd4ofSiKVGqK1InqIaJk4lYovgcke+ClDEgRPdqHur4yWktigbgxs27F684PmP/Rp2H3yKe31knGbS8R1xPONqZbAWM41ImJComWgtkfD4jhwmku0ofiD2N8TkCNEzngxxptWyC37bIU7w3quPSs2kuLQ2Ph3jSuJrom66BxBbOVHEfs2m3bAojQLWUqkUFSzrrcEbDdB0M1L+1RJoG0R7tvWJKmpIXX1aTBNt64bWbeYco2TqnNaSrWarLcVuROoV6TG6fWqZrrUBfwMl2cv1XsohXwtayBVs31CnpfT69c7KXxfaLPdz+WwlqF4HKE8DjfYb36Bs9fTzWsiwbHgsG3gLUHJSRGspITfZfWe1OULabr8iYsvZL9dstEy4BIYKrRhM0iiyFkVGayP+qipwIeaKc2ps6dbnpp+0dgU1c8SchVwNRdSF2XtLMamtyxVr399y2zqdlXsyTicE2A47nLN0xmnSOs5rCXNRUq40yYtWmre+I5VMTanJKagWlViD8x0LqffyTK8Uf5d7UvWeaqmnYKRQSyTNkeIcqe8VLRkGpQnUqlwYqUzHSUtcUcn5qsXjsKZrZaxvfvxQBCjWVvqhsN0K28EwzwdyqozjARFhuy3QC93zir+dsdsJ5pGcBSfaSz7HEzHOhHQil0AV3YJKhZKNmkfhIAl5gtObzPkQuf9swvWwux1xnaPrHc5VttvA/pnhxbccsgezMRzqEUzldM5MIYFUrBM2g0eKtoc+HidCSPjmNNn3lTBVxjGRfIZWn68VYkjkrF0+JWsWJ8aQYkMdimGeZ1LOTNOkpZPGYXDWNWQhq7R3gTkkjqcz//W//XdevXrFz/yvn+F4aOhJMQ0GVL7Fw8ODkuWMx9oN1vbc3Gx5+fKO22c7us4RGzJgpGj2YXbcPXvGfrfXcpIxTOdRy13ShI3kIpLknYPVQl0PY4SN02xBWwV1KXLLQlEqVoTddsNuu2W/2zKOI3MIOKccjX7o1NdFBO/AiGaBxlhu9rfNpMyTc+Rw+JIYCn1NjFTGWvFYkrWE5ChVCFmJdcVYqpFWSqBB1bpT5AzqjCyI8arJ4lQZ1g9KXO5CpBTNVvr93dUIF7aDRYrjbuPwVssTpVRCKq18UyinRJgLcVTjy5wsGQgC296y3/fcDYaNM3hRgum7uZKNJWA5JsGeM+UUCMPIF6/fMZ5nBquck2G7w+aInXbkg5ZuStG6/jyemOaZN+dItB2T39NZoTeGEhPkquaDJfMwTUhn6He+cWMyMWrra8mXDU87YFTkCmcxprbNddkIrxdYRRIXMpc1StqzouWswYG3sHFVy3wpU2NqolN53ZQWkuwCTYgYvL3wTrpmgtYbbS2vqSIpq0hG6+56ajip53rhoNBKRBp4qY7EN20zvkYHljKSomOG0ngW+uMiVTk8TnVxNFtvVgqrMVD9uvjk8kktMBHJwEwtZ2o501mDOCVtO0HJyM3Hb+kMqSvE8ytHLd57fuxbP8IHL1+w22zovFeEL0XCNCtZuRRFx0X5ZlIdpgqdL9iga3VeNWa0dXwVwpOGwLUuFFu0tKG8PWEhzVdgMTrMJRFz44cZ2HRGu+dcg7dFVBrBmLUdN4lD/IZhL9heO3lyyms3Ur/bP7nunBIpTKhOdCZMEWolh4ox2iEX5kDNen8bkKcNEH2P8x39bsfS4mdSpOTcTEErxTRHd3NpxVc7APCu0yBPtEvIOk+KhWnSe2Il0xnBS0uGY+F8OmGsxblO7zWqCWMFlaxofLdawThHLZkwHdo8/ubHr3qAknPmr/yVv8I/+Sf/hM8//5xPP/2UP/SH/hB/4S/8hSs4tvKX//Jf5h/+w3/I/f09v/23/3b+/t//+/zkT/7kD/SZCnMpodR3QkyzesmEgLVqlFSdwW0NblOwg8LqNQfM4jmRo3IEsroR17WLnrV7RYwFbXUnx0w+Rc6HipjK8W1SqM9B1wvPn0OcPK4T+lrxIuQSwSRiVDl5Yw3OGTpnoVbCnDgcJsYp8Wy/w1nBOSEtbsumrgEK0GrBWjdUqFyJnDmro2gRhfsKEFNsGWXLApvPRW0LcjEK3Y7jxOeffc6XX77mzZt3qp+QS6u7NiXGKszzhDUO7/V71ho2m579fsNm0+GcJQR1NxXRWm/nHTf7Lbe3N2w2GqB0zqnPRUikKqg+oR7WmrVUshymkbGS0dbpJdc10truRMtNfdet/JvYeDfGWaQo4mZNVRQCTy2tc0cs2+22tSZ3pDgRR7Cl8ZFy1TpwNSTRkmAuev2lZVNrQlmvwOa1+lYp1bXv2pWsu7TziVPxOuscrhsu4xtl7Fdv2HcWb4UedfMdkyoAx1CI50yZMuWYKKkyJW0XZnDI4Bg6x83Gse8trmkpJAtzFU4Ic6ocpkQ3zsh54nA8M54n2DhcrfS2tRT3njRaUkOHSoUpqUnm6ZwIxnN2gf3Q4zY9C6u4FA2iT6FgC5hO9StyKuSk8Pz7GXlpHVEsMuxy+dlSCrCNFGhMXrPlpY5uqc3dWcnBTpQNU0pe50stF2hfA8uFsFybCJZh6fzR1lYl1lYg16wBT3NJltbH3MKby2BgQTSWNaWupY5v2mZ8uQPL6G8ljdrCokYEX38qT89ncfteSCNXt/Tpn3L5q9bKVMVZ8cOEt4Zqmy+ULAHYVbDzhOPyKwco1ljubm642e1UrmDxFctJVXyLooPGKuJtLFBUl8S2MtlSlln4M2Yp73FBdUwTOasWpFQS19okF+6S0MwGy0UGwTujnlS2lYuuPM5ys5/AOFwnbDYG35U2vpM2CeRMPwxPICb1QdP9ptRCbkhiThqgLOKZF7k9vc+GJvHg1fVZ9aYMSzeVqYqqyuV2tA9s80NagG8u4nfOe9SNulKNCjVenKszpQgxzBokWn8lkU8ra5b1sS/oWymJkkcdbz/A8aseoPytv/W3+Pt//+/zj/7RP+Knfuqn+I//8T/yh//wH+bu7o4/+Sf/JAB/+2//bf7u3/27/KN/9I/4iZ/4Cf7iX/yL/K7f9bv4r//1vzIMw/f4hK8eOSWmcWI3eMAxz6lB5aLSyyESxWD2lpuPhY+PPV/+zMz8kKE8U3guG5U8jpVcl8q39og3PR5yVZJlDrFhjwXJqnORZ23nMkbBhldfwttfnPnOz555/mMdt9/yPPu4o9929DtHv4G+i3hnGDrPHBLjOTKeYZyFzlSGXnj5bFB10xio7ulCtkTtC5arfJREzpYkAkUYpxFjLedpZEBrr53v2O93TOPIPM8M/YCRyLe//V0++/wLfv5nf4HD8UgIqqq6EMe0NqQLWl47GwTf3fHyg2d88q0P+ZEf/YTttoNaGcczterCvtvteP7sGXd3z9jvd1o3BXrvCTFSHk+QVSlUFXeFzjeRoifo4KK2uCw8y2KkOgr7nbYpPru7Y7fbsd/v9Zqd05psKey2GyCTQmW33fDyxYeradvr1685Hh94+cFLLf30DmeKlkaMEuW64AhpETfTAKMAaRV3auJytcHWLeuplQufgbq62Nb2EK31QKUOPcNme33R+LbI7BCGIuyqMI8Z3gXO94HpMZDOhRoqLuniSy7YahkGy97ArRfuBsvN4PBW0arNreUUC995mJnPBx7PM4fDSL/9LqdzJKXC0RoMha7MmDBix0ieEyVmHA0pzxlboEuJQkRCwtLjpNfn5yAPHkmW+zFTi5BigVraeyjycdWw1RCUpG7c7d9LEGGWYNRZdrt9c/C2mumVjBFVBjaSsRV6q22cOYTWdqtdFgrYGC0plGaksOzoLXNWgTItO2jnT8EWre0bDJ2oQVuidX3g9Hm3Gt/qYE+9lHVkUSpd+GM/4LHsQEUHmFRWY8CSDTEJ06yeWr4hJ9N4Zm6oxOoJ9HXv3cCP2lz0rOvw3cBmO7DfD9zd7QlzQEyiHwRMophMJiMkfZZ1KcP9yhC/IgIe36m+CVUdynOYyGGEFJGS6J1dNUOs0/nmnLbgjrM2HPROayDSYMxF4bSWjMGg2nXLLrp49+jzKVcu17F1mJnmebUZLF1nVeiz2QhI+yxTYeg3fPTxc/YvHR/9aNfasFU5O+XMaZrZ7bZPOEcL1aYuteCqPERtDtC/993Adj/AQtSOMwi4JtpGaQGFNA0eYyFoUBRigCykpYRVKlItql9isVUVu3X4VFVqpqACctoCTWn6WhVy0qC96x2n84nD8chjGKEknt8ODEOnZa1SeHy8X0n5IZjvOQa+7vhVD1D+7b/9t/ze3/t7+d2/+3cD8Gt/7a/ln//zf85/+A//AdCb8NM//dP8hb/wF/i9v/f3AvCP//E/5uOPP+Zf/st/ye///b//m39oRdudUMXGtUFKdDCm1o7lhspwI+yeW971hdksrbOiYlcYWgGZ9b8asgK1iQXpgJdGyV+8U/S1SorMQJ6FGCtTzJghU43BO6GEpvfgtZZoEEoWcpJmfNaUIYtKgSsLvH69MuMlgbrKznSA1Sa1nFJCSiGmhMuLNbjgrSU2DsrS4no8HHl8eOR8HhVWbMGJTthLS9ryHJf70XWe29sb9vsdu92Gofdasug6QNGdoevZ7XZsNgN93zeuCXjvlGhlLbbCxT+4ZcgsD+FyyJN8ov28+dCo54/De4f3nr7rCF1HzZnYd5RcGIZea9kp4HzPZrNVMTdneXy811Zjo9mos2qyhrWqJCxCyoqUOKMLhm0LoYHWRXqVsrTrFKMbVYFVCt8s2bPR8bZOX7N491w96obe1KBlDFNApkw5ZdI5E86ZPFX13GhlJYp+hsr9KwxrWKog2mUkS0YFkJOWRg8HwpyIOj2IoiM85oBNM25O1KTcn6U7Qs3+oBedH6EmXDaYBMZZ6go1a0lIxxAsy9bSGfN1W6X6RS3E03pJzGvjGhjt2PB4VbvNS+ts64wTtVmQWhvCqEZ3TSqiQV3LTiFPx3sLwtcsdCnT1AKY9v40sTdt2ECgykXIa3VJqJfs/Po6/x+EJ8vJr39XcTa58mdaPmfximo8jnLFIalXKMN6MvWyCrb2Jmn327Bcs1AsqCpyxUhBKMui2JCH5Qb/yketaiqaWxdOrVnRhaJ8Lapac+h4k5bEoHOl3eaFpNx/5b1pz5G2Uku7vnq5hcvrWLqRdMzlUjG2aQet/kALp2Xh8uh89bbjbnPHVgZu2WiAktVZPuXMaZyUC/IEQalr5xJLsNz0fiq6/ooYnLOrBD2rQaze5JJzCzh0ngqV3BKgxWakNoMh7b/Q51lKYrEU0U7xi5UBLTnX4K6pYLdrrVxECGspzHMkp8jtrr/cw5KI07iOg1p6/o8IUH7bb/tt/IN/8A/4n//zf/Lrf/2v57/8l//Cv/k3/4a/83f+DgA/93M/x+eff87v/J2/c/2du7s7futv/a38u3/37742QJlnNZhbjsfHxyc/tyL0Yth3PXebLUdJmjmgYj0hF7wT7m4MJEffGx6+G4jnqqWbarnpd5hcOJRFQqltKkYVGqspWsultEyrZcPrgqM3f1FaLdnq12yZxsCrXxi5/4Ut+9uOH/9Ne7odsI+IrRxNQdRJkO12oB/AFhXFevjyTA6FwRu8+ZqJXi+9/zqB9Js6eIRxHCnA6XwG0YCFWvHOM3RKPixnld//2Z/9OT777ufMY9Ds9v3FT6TxOHQBMQa63vHRRy/5Db/xJ/n0Rz/hxQfP6Z1bF745RM6nM7e3t3zw8iWd75pzsWY1qXUvDX0PJhHK3NoNlRi3Qg/rIdRqqa3lWjeB9poWaGmZRxh6z81evYHmvqPv9LyePXtOipHXrwx979lsttze7dntNlhbGccTYiolBQiOYpUE32VDyoZSAqZmsi1YKsUXQimkmltAJ+iW3RQg6wVeX+Xxl0WzBbwLOq6Tu9W410dcmY+J6WHiYAKx6iY8njKPr2cez4mHsy5ABlRtFi1DdAa2prXMxsIYCsUUHJ4CvDvMxBYAe4GNZM6P7zjEd3iv0toR7W7KMUCMmHmijzNdyjivWhQ3rdZ/64W5wiFnchnJ54nse4r1FNNRcC1C0qXOCjgRLFlJmF8Z4kbdZFNogYGWR2spGNGA6jyd6HvPzd2GnBw5R87HMzkmeufoRLCt1XYKzRitJYqwDp3LM1iH/CIC14wKqia5GWBOuml5R+eU75IEpKAGk61coO3QVdeHtjYILctdh/T33sB/+aPt1Mtb1Esq46wwdELoweWKmJGSEtFpIBFC1NLamoldRtzlq2BIOEkQJ8r5DOcjMh7w+bF11hhcijgShkXZ9br77ntfX0yZz758Q66VXxNmUorUklrZfaLWAEQWrRpDU7d2i26UkBuKtIwh02Ir1m27ISfoxl3rwjdpyq0LQpELKRrGUAi5MvSOobf0nVMEJre9gSUIE7qu52b3jOc/8htx/Z5uuGHxb6ptjZ5D5PE88T9/4fMVNYtR0X/nvSKF1iFVyEU92wzaMoyFUiKl5sbLUx+kmgt1GvGdx4sn1bx608Wk54+puKw8O+u98oOoxDJCrqo+DThvVzKxGLXPSBl1LPOu2TUMFIR3hxMpZfqu53A/cj5Fum5mzoWbZ2esZKb7jO063DBAdTzhJ3yfx696gPLn/tyf4/Hxkd/4G38j1irk+jf+xt/gD/yBPwDA559/DsDHH3/85Pc+/vjj9WfvH3/zb/5N/upf/au/7GeKyNqiqpEfbWNoUXIxDZ5KGAv9Vhj2lmEvuFkJU711lNKz3205xYkUtK2WuvznauVagpIFoqc2o6hLFlJq0e8WqFFVR8eHTE2RN9890e8N3QeJbnBsb7pVSKksHTmlyYxPASnyNWaBtalrNpKXtLbhVte+ds1UDYfmXLxoF6DlHuesTpJp4nQ4cTqNa018RYauoGnT2olrVQ5N33fsdluePbvV3veuU25IrfT9BmNVBXEYNm0xMJd7uPBhrMorp6IqkReBq6W+efWsWeBV2yT4LbXxXERQUlYIPNzfI6BEWzRg6bquLWC62YRZTSSdm9lsBsqm0A89YgqlBLLJZO8Uvi8LL0HonKF4SAlEClFjVmzR8ytFy36qssGltXN5cleIl1SFlfXPhkwZvrJpxSlrkLKgGQXmMXOaM1MspFxUD0KWurF+iG5SHt95jO+I4inFkibldD7MKm/vug5rhcELiUSpGd84B12TtQ8kconEqJ4ummHqhl2t3lffkIIsMOfKXCoxZ1IVghjVVsmK6tC6oFgWRXkfJ5T1Pj3J9LncnwoXVKQJ6VG0BGBsU4sVdCHP5Urn4jKdl4y5PpniLTuWBe3Ryb34AGlLc8WsJRJ1uHVoVxeiolUlZ3W4rctcWhKaqxH9/yhAqWspaQVmpIAkfFfZbg15KwSgzpEYIvU4M89n3r695/bjkZwLrl3fYs550eQtOFfY9Im7bSbvM+Vl5dYL4cEzTcJ0drzYOba+aPt6KZhW2vk6BPRrr6JWYlJtleWZUy5l69oIvUuId32s6WFdca/1WS3JQLlaE9f51163opztq1VHaQK1eCdqKWFsQ9iavHs7DfWM6uk3O26fvcD3W7p+r0hVU7RdNItYZRN0NJSSlR8ol26uBQnWQEGpBteeR8ZqiWYRZ13nyEq2FhDbPHGWuHhZk+rKzRFjW1Cmn1sWnL5a7RzNYIqiK5I0UBnzqPQHY1ARBs8ctbx2mlTsc54yRhJ9FxXtKQsh+5sfv+oByr/4F/+Cf/pP/yn/7J/9M37qp36K//yf/zN/6k/9KT799FP+4B/8gz/Qe/75P//n+dN/+k+v/358fOTHfuzH1n9bY+g6ZX3nnHSzMlCSPlTBkWNkjCNienY3Pc8+HCA43FuHi5a9OIbB0g/CF+/ecH47tndvcGAzCdOFS/kTrJuoMvrhAlNXcoN0UZ5KdRxeR45vZx7uD/R7ywe/bsOLj/Y8f3bDXCbmODFPmZgLrgolZo5fnhj8wMtndxh5GoGmPJPSrG3V1uFtr14/7YvaxKza62utxJadQGnGTp7z+cy7t/e8eXPPw/2BUpYpvzhrauBwUTGtUAud77i7u+WDD17y6be+xbO7G7ZD3zLcyn5/o4vDbVl1H7TufpkgzmngtWkuoGOI6iydVOtCVWQvIYoYg3c92fdk15Ortq4aNIBIMXJ4fOBnf+ZnePbsGQ/393z00Ufc3d6y3WyWG0GYI/f3jzhrOJ99Y/jDZtOz3XXM4UAKYOKGHJXDFKJ2XuwGjzeaVYeYKTVhCuRsyNWSswHx1BagXBLUS3fFZVNspaFaoSn8knWBuT7G+8Dh1YyvlSjQO+FxLnx5ypyTIjjbRljsrXZVpArbzvHsbovbDLjtwIwGCV8+zkwxMydL3zs+ubmhc5ahs7jjxDQGbDhjS2bTaafJOUdO88S7+UCttKBDV2rvBAf0Vtu9PXCMKsF9TpGxZB5LZqqq+lyrpZQeY3WhldZx8XV79QWFWIJau46JWispajJhxCA1IbXQGcH2jt6AabyxXKrK5tdLJffJ82jZtVnbk5evtpm0Da5UVKhOKjlV3XQEvLFtk1HJ9e12S4iRWs9Kwl0InFfXZdYywTc9lpGl3U+rM7MRxEbIM7t95eUHjr5awlkIx5HxNPLm1cjDvfD29L/YvvgJPv11Cdsr6pZZkEgd65XCpo988Gym/yTwrT4SXhrC5PkNH91wPFQ+/2zgxSc7PtwW9ibhSsLkDrh00Hyvo9TKHFPjQCjaVEpRiYYQNBGr5YoQe7kLgvKCQFqSKOt7rk7vdSl7cLWctAd8hUKZhsQERPllwNA5Np2jt55FBWJxVka0BDTsb7l58ZJPfuzX4FyPtV2TE8gsuiopNVn4q/sRY6RMo5L4jSZqKrCpMgfVqADdHFMLHxziO0SM/g56vcu1KnLVvNesMGz0Z4VFyVWbOZw3WNshGJoYOClJU801HOfIeYwsMUts5/DFm3vEWoZh4Pntnue3NzyMX/JwDBSEeYYXdxGycLdJSDEqhvp/SoDyZ/7Mn+HP/bk/t5ZqfvNv/s18+9vf5m/+zb/JH/yDf5BPPvkEgC+++IJvfetb6+998cUX/Jbf8lu+9j37vqfv368sXg6xguscU5jJjyeGrVOlwaJeBN5vCGHkdDw1Nr9jd+vJo+P8MBJiYIo68De7Lf70qA+9TdZrxv2Tz5U2Gaqsr1vqd5faNkBd7cprrYRR3/vwOtO5yPlxIhGIRE6HiTlm7vZbpJHTcoVpziRT1e+7HSUHSpnpvFW9EAy5llWP4cmgaAttaf1qqkFmMEUIU+B0PDPPgZgW75ulhr3AmPo2tWWxtaqyadeeTd/3dM7jjFdoUyrW6YJul8ynBSmloRgCGqCYQlfVGtwKq4y1BjRPVzgjBufUQdNaS0nSTNsUx8o5k2Jkmibu371jnlXqvuRM19xqj8czx8OJ11++VpTYG4xVV8/nL7f4zhJjIKd0EXgSo63JVtvanYc+a9tyRI3v5pgxuWU36nTD2sgpmVovmNRXxrBcck2RRcirHZWVf1IEsmg5IRaItWBMZSuws0JnRDfl9uA6b9hse9xui9/faGBQDYQjxlVe3Nwy9B372w3eCt4C/YSfAvHtF9QwYppD8yCZagvJq5FlaehJqJWpCr4KRuy65lsqnVR8MsQimKTJvVmJ51b9T0A3n7LkyE/ujHYa1AXFMytgr6ijzr+SK3GOOFNxUjVgMiBN46QsfKr8BAu9Kutc2oyfzG+WYHqRVjErWrd0+uhrKq49slQVTu/6DjFCCIGU1Fbi8kgXqOZqcn2jQ1r2LNCIjvpWVp9LVNsFqYWh7/B1A5N6IuUUmdPE+fzI4+Mjh4cHXP8M110tWW1GVRb+R2HoBtzujlj3zCbxMBjSXLUjLCaOh0emeSSlgPMDBqfXWb/3JS6ogWmE10ompahGcyWztEQv7yPSCMZVYOG9XO7qGnAuS/bS2VWvIxsuAepSJVMujCZYyzzsm9q1rJyttpaJaWgVYEzj/GWQpGi3aCCzdCRZa+i6/gkC5KzVdWlBm9qYNlYFAU3XUUVRTk22IVflwtiua8R9p+tK1a64UhK1qrW2s75JN4iqk5uuqVEbYlbe4+EYCCFzOM2IqFP9m7cPvHs4sb99Rtf1xJQJMfH4+EjXdfTeMZ3P3OfE4+OBw1H1W0oRzqPgbOV4HPHboh2I37iVvt2fH+i3foXj3ATBrg9r7VpW+Imf+Ak++eQT/vW//tdrQPL4+Mi///f/nj/+x//4D/SZxhhc5zhPB+LhwKebDxl8T5dAmrbF42Pl3VgwvT70m7sBky2HXzwRa0GCo+823O1u8P1lptarwOK6JdAuJYrm/Kumdjx5jbTfxyzwm06ydBZiEuRVxrvI6f5MMYlsIod3I9OcuN1s22JoKUUYp0T0161alZxnSh6xpl/JaLVCzLlle3U1rlu+Ki1AMWDQVXeaJg6HI/O8GEPpi1W4Z8moLoSw2qJsYxzDZmAYBjb9hs71OOMpC+rxZBjUq/tzgVe9c9iqYUyIaTVVs8bwddu5diH1JOdbJi1rEKj8hESMkWkcOZ9O5C+/xFmFMvf7HVT47LtfcDic+OLzV1QKIgXnKsYUhu2HiPSEOVBybAq5tm2OpZXFGj+nV02EYkBSYRIhJg0qS7XKt2DJc+vqxbJsJotv0dKJZI2a/VUrXxV0ihVCpWjDGdEYYtYApTNCZwz7qwBFARuhc5bdbsDd3OCevSBltXeQUUnJH/34T9APHbvBK3+gZtxuJs8z99OBVAKmRKQmrCQN5HrDFGFOWj5MOsRJi3KkaGnFGuisln1c0lbtJTgRgWoMpS281Eqm/f36ebdgtTbi8OpbUi/ihFRFS0MqGG8wXu+Dt1BjvpR2WpBClVUdvnK9kb334eu8kaYwLauT7QKVK1yuO4czGtbHpGJ4wzAgYvA+UHIg1XxV9mCNu3/gCk8FLZMt7Z2KfNZSW7dSRkpp/K4N6QCTuQTwD9MDD+/uebi/Z/fsWwy7y/noI1FibW0dHZt+i9TnxHrLbCLb3hJ8wWJIIfJ4f890Pimqe1VifnrCX3+xS4naOYu1Qq2ZGOdVnG2tv8nyDvr+uWRWSYi2sCwBVrlei2UJ/NdY5wpIuRZxg5g1aFb5CmHTOzqvuiemle9oAQQ0gqlY1USKEdDuLuvsWr42xuCzo+tOT26Bc46h65pchHYaIQZxBusNbvCkIk25egmodP0d+g5nLX3ntWs1RnIMGiRRQSyd78AYKhqUGG8aOmsYg3KyXj9OnM+BV68fsdbTDRu+8913fP7qNT/yo5bdvpLCmRgjj4/37LZb7vZbzilwPmTuH+45Hk+U4sjJcjgJhsLWT2woSOcaFeGbH7/qAcrv+T2/h7/xN/4GP/7jP85P/dRP8Z/+03/i7/ydv8Mf+SN/BNCB+Kf+1J/ir//1v85P/uRPrm3Gn376Kb/v9/2+H+gzc41M+QQu453DDw7XGcohYS0M3nGqhvOhEMfI5CZst2H/wtHfJmKYef3FGYPn7cMj98eDnqueMNBg5rLYpjfBMlEhpMtms/zZJsbVZOLyjkh1EIX5AIcvI198+5FuZ+h2hkE2dBt0s5CK30grgwdUBakdFc0MUybG0Da5rhlHXWDjlFQimaZVEpt6aW0idDULp9OJx8NBvTXaGmJaf+WqTMvT4At0w+i6DtdUYNfra2hDXTuirjObpazhWAzwoPHdrKfr+lXzyjp3yXDWY+HCOKz1K7KljPfWYSXQd155CSKEaeLx/p4wjdQKb9++Zhxncknr5pJyIsTAOE6IFObFBLE272FREy6DqgyLaQqnTSG3E/BJUGcQT6odpVpSO7ey2Be0gKpWkCclwuWbogHRe0G+qbCIGydgTNoiO1jLjRP23rBtokrU1ilTTVPYBOs93WZHz0ApFns/Uwpsbp5jreOcZkpSvR1vPNZ74vCckC1lesDUgJGsqtg+01vwBQKWLEZVJsVwEnW09lSSGLJT9eKtOF4Ww5QLcpY3pHkAAQAASURBVDxSzYLO1dXATcr1SOEJurCa03HhEC1mjbVVz41AZy1D57AEJBdS1jGe6wJ1L/eaNRiq7/25TrA2/6UFj9bKatFgpJVBqGs3h7EGixCKdjEcHh8vvJgW6Kwy7otexTeQuv/lDkG46NtlYjgyHz5jHo9MCVzdIhTyZMlnYRqFeS7Eeebd57/EL/73/w83ty/Ybm8wTknMGdQnan5HPD8SzydqrhjxpJSIIRCmzDQnjrFiT4YJy5tXn/P681/i5Y9tGZyKTT69uq8PUgTBe9fKzpmSA2E+k8OsRNy1XNIk1BcBnqXNUBrPpNQVqdJ4RQOKhb1yvX7BRY0759Rc3SuldQYZp23Fm6HT1uXGYymlNLNUFWiL2fDlq0fq28z/eFV0D2pf1lk2Q4+zFmeE43lak3U9tPYnLfjS82uCg9ZgvMdVg2AoUXlUpUEpi7Kvsbat0YJ1pq1NohmyDa2U3lPFk+l4/frI/cPIu/uROWTmJExz4NWrA13fsd+rNtGm80iaqHPl2ZDxW/i/PnyG957NpukW4dhtPmaKlVI2OOeJeE4h8vZh4lYMfvCU9H9IgPL3/t7f4y/+xb/In/gTf4JXr17x6aef8sf+2B/jL/2lv7S+5s/+2T/L6XTij/7RP8r9/T2/43f8Dv7Vv/pXP5AGCighNZUZ74pG4KsDsGatnbUYDHGCYivJZvbblt1tC3aInOZHarRImZlzugowLpPpusSzZF7vlRTb67iUV+Tpz2sVbSPLQhoL0zHx8Dqyyx1ienzXYb3BSAIK1hsNhktZ21HX92oeGzklRAxVVNlzyTRAyItRWE6k4Bq5Ma+kqlpgmmbG89iykbaANHSolqZGeRWAXRZac+Wr0+Sf288N0n6PqwzvCoVaNx9ZYXRjDN55nMsr67yUpwvaxT3UNnVE3ZEvsL1Cs85abCuF5JyYprEtcoXT6aQ26OvV1tUSIMSIDcqul1qxptnJmWY2Z2or+ZQ1I3NicFVbktWfx1KzheowtTQCddbAr4n9KYRs2oKnGWJFKX5ivirhLiwQtJKnQ2uT9UaVYW87wyDq0xGLBi+ZNg9EzfKs7/BmIFaPOK9WUX6DGMM0zqSoqrS7TU/vLcnvSL5QYsAUwcqkwZm1ONtmhulIOLJxFFFOiqGq8ZhYinGYYa/aO2LxOTOVRKplke5Al+Rr3dWr6766DaW00k4Lausy16hoq6tgLXROMBklfLbss7QApbYm56WsU2pDDL4SnMj6+Zo0L/yXJWDRs1WO1iIMpwGkFS3zTOPIKtTHUra7kDiv3//7P57eoWVMaKmnksmkNBLPb4lhRIVSe6QmymwoM8QAKaoR3+nhDW++8/OMj/ekONG5XdvsIZdECifSfCbNU5uuKnWfUyZFVQCeU0GqJdYjh4d7Hu/fcPvJRFd3qtOxoFWXmO+rh4C1S4BSqUWNO9UhPLe1aim9XAX2V8TQheux2n689wFPeCstRiyLu3sTvUypgm2dgL1rApMqcQ+0MaWrjbojW1IWXj9OTCly/GzEteYD/T3tzPTO0nvXULyrM6uoyJ+0dag2mq/QkBqLQUUkU1FZubzUKduqsLgJV2iia60dWiCTViQoVfXHevcY+eyLI6/fnAgh43vlSj0eZ4ZYFL2ttXUtJUyGfSfsBssHz1XHpUqh4ihi2Wx3JDzHyVGKEAoQK7ZEuiGSQnx6zd/g+FUPUG5ubvjpn/5pfvqnf/qXfY2I8Nf+2l/jr/21v/ar8pmuE7Z3lq43+K5SbSDVxPO7AW892x62fc/N7gNKHii55/HwSKmZT37ijhcvd5T7yuFt4PV3RxLalXFRLFgWk6uZ1e53KdqmZ8wl01Pmdl61UlSAqpHnhDV7klwpk2G8tzij4lm7ZxbvDZIEbMV5T9957m623OS9ps/t41OuxFgo5YwxqpSaMeRqVp2LkjSASXFmtpZpHIlN4ySnQo6Zx8dH7u/vySmv2cj7a8jSVbNmgCyQ6SI1vpiSaVCiyrY68VPLflJK6+uXQIPWhhujKtR63+GiCm2VQkOELoPbiArN1WGLIZLDgVJmrbc3FMII9J0aSO73e7xvfJWmsvvs7oYQE+OomVPMQXVTnOPweOB0UsMsY4RiVCcHbHNLVTnnKoLvvQYMKWGLFqSctZjWTluzxTVBNGvMivIU2sLasmorTSjqCkExqx/Mct3Ni0agCsyN1e+N4I3gROistuz6tt13KJnWtCxz7VRBEaopRH7+579NqYXj8bxulvHult1uQzA9qddWZdKMWnGfoXmfdAZ2+xfQbUh+T8JwjpV5nHn78IhYi1jLzd0H9Nstu74j5kiQQsqRVLVtMtdE0gmnAVU7VHbekU1rOV/CybbYLXuTMRVnYfDQuYI1KkFfimiprSrvuIp2vCxBzcKnWj5rCThlNRl872txeTXSXJbrSs4WEapV7SGtucOUExXTAk5FYJbN0xpZP1O+Tj7g+zpkRRGMMSrNPj1wfvyC+y//N8X3dH5DGs+UcSY+TsSHmThmclYNpPPDOz77uf/F57/wM3TbPZ/8+E/ghx5KIM1HHl9/l+n4jjwFuk5bXst5Jj+eqadIOSfmEZz3OFt588Vn/Nz/+G/cffgjdL7D7p7x/RjFGRF67+l9R+86TIUcAikG7RpLqq5aS6GKllnVO6finWHbe05zJiYUtayo4FlWTklKhWyedoXlrK2/IWZ9XeNUGQFxhu3g2W08295pZ2IREFVu1iWwUMQTquH1CU4hcT+GNlgWfmkzmGyo23YY+LWffrKiZtKCa2+Us5KVgYvzG4zvEdO1krvBdoItBULrWEyVXDJzmdT1OakKtTWGzW5ArCGkqEFDstyfZ149nvjZ73zJd77zlmlM1CK8eOERhJubHb0zDLby0cst+2HP1hc6W3l56xl6y92+J5XKOWSydWQz0NstVTr6neU8Bf7Xz/wcjsTLjc6Nl/v+PdTo+z9+KLx4xIBxFevBekNpbrAb5+icIKgKqLM9MXtKccwhksvM7e4Ztjj6Xcd4yqSSm/nb0/a/RfRogYdXnH4p4tTK0wBmaf97uhC2H+pvVW0rjpMQJ0hThYRuiMWC0QXbOo/3HRa3BijLZ+qCp50k1qTGfWAFVpeW3Zw0QNAOmcW5MzelQ9WZKb8C0/or6MdyX5aaer1+zeXcytoqqJ9vm2JrzqpHIS0bKE2EaMkG1KOkrgHg1QcqcmMdtYmraWvyJaNd5O43m4Hnz5+t55uaouN+vyfEBEzEFCAsXUYQ5ghSGDYeawzJmlVaOtcmGNbOwzRpe9uIqmrhItTSDLyqAdv4DvZSytHgpHLJplvr6hqgtE4pLrDoIlC1DLHSULKlELSGM6ICWsvflxLPWqNvz8xYB5I5HI+klDmfz9imxtv3HcZaYi7kqqU3qNhuQHWAVMjOWHDbLWbY4YZnZHGkuRLNmXhSawVjDNn2VDcgrRZvnMNIwdWkoorNSuIi1tYeNYvtuzwdZyyXUtd7Y40GldZUZEVJFr5UC0gaalcX5OYKslmDkyvUVBaUr30tyMdKmqWd26KJ0V6n/C4ozYROc5T2nkuQ2Dax64D/Bzvq5c+aSfOROB8J06kRyTdKmo+RMidKSNS4JCJCDJHz6cjp8R3H+zfkTz/FFUvNgRwn5vOBPM/rAJMCNSRqiEgs1KiKzGKU9zCdTxzu3zCdD4R5ZNjechmll7XyK4do+Uz9vdQ3pjRTyBXxrRfEbJ1LVTsMvUM9ZupFgK+wdPJoF1gpi3pzKwUtwcsq3NeQ2PYcO2/ovXJiVFiymeg1xAOp5CKkKswZplSZ01pEvKohLs+nkArrvV9+KCytv4pYIwZjHYsn0DpWrFnv0zIRtMzPatnQFhuVd7AGWw25Vqa5cBoj94eJ43lmDKoSLVUouaDGi47Ow+CF263j+c7RS8CZwr439J2hd4JkGoqi8vrSSGVetIFgDBOmZHoM05w1sFxR/W92/FAEKKUkQjrjssNlx+l0RnJl2KuL6zSfCVE9acYpcTxmxnii1Imb4QjGYreC9LIq8C26BbKsNi17pSEDunZdFtAFMoZLqYGrydB41O2ML6SvMBlCaJMnV4bB4Cxge2x1DJsNJlce7yOdyU+Ip6UIpVzY6TFNVDylerJoN06OqvIX58CMcHx4ZDyfVLApROY5cjqdOZ1OOshbWWy5hidlrbZIXItDyooz0eBVbUkrRXUNtM8/rIHSEknnrIRBb3z7dyWV0oymtNRUivpwvH8OmUWDpWfoPTU6zVSb4JhDdXE+ePmS3/CbfhPjOBFiZJ5naikM/cA0zfzSL32XaZ44jyecs0zTSMqBpSPAWQudlmOUS5BXxWIRg22OxxvnqBLZhMwctazWdxs66Yhtu0yiSBEW1VcxSzDYUCcqppWz1B+jua61wy0y+082ag125lJ5GwqpMwwYbr2js8LOVlxvGZxygqix2ak7Xnz4Mf4083M/93PM80ScRjWR9JZ5PuG9J5eISOX5fqAzFru/w+QNst2QyUQp2NuXyGbP/uWn4AZ6eszbe14Fpy7SMZLngqsTpt3f+3kGIp1vQbxUJbJCUydmGVytJVIX64XMGkNeNyhrYOgVKdr32uJMVLPEUpYgQAO0Fp6tAcs6pxscbsxlhmqXmbkEIUvEVy/nJqZ5s7QgI4uKbFnfSkypghjEOq3OWog16rWu9Y73tV++ydE2aalILZRw4vj5z3C+/5x5inQ7y64bKOOJ8vCOejxSj2fkPFGqIfjSSo+Z73z7f5NS4sNPXiLlGfN8Ynz4kvsvvot3nr7fYkpSW/XHM3J/xp1m5BA5vYtsn3Xs7uB4fEP9IvHqOz+LmMp2f0dnNnrxT/K3p0GKIig9QzewGTaKPFbVCAkx6DUawWSjbfmtPE0VNr6js0rcPofIeRzbnFLNnTlkOqsoY6oqnlaSoiaHMXEOlcNcms+StCEhPNt59lvfEBADRu0ybNWNGKlMCU4RpmLIGBWDXIZKaYlaQzBLKYhZzCGWx18Rq6axRoQctTRsfYe8R5T33mlXUe+gFkpKUCoh5FZ6ohkbFkwQbOeodstxnPkf3/6CN4eRz98eqRmev7gjjJMqbOcRyYrk7nvHj3/U8+Eenm8KdUyQCxvr1dojFFIxFHpCEgKJNB10aes7JBdubraEKfL2OPHiCKeTIfXyg+i0/XAEKKCPvHMDu2HDHLQ9ruRCrJGYJ8ZZuQCpZGLS6LkiOvijJdRIIlOusipgXda+7ljktJeSSL3+Hsv7SIt061r75LJEasCTIc6F6RyJsydFQ43q7JEnTzEVJFN8edJmfEmK9dNrE4eDFlnXS2lmIYOFEIghNjQlMc+BEGZCCE1c7umbL1nKdaZ5lWM2VGnJdlQBUcXgLpv6RXTt8u5LgCLN92Qht6UW1MAloLl+AqVm5jRh6owpASeq9TH4rYqdFaOiZCJqbLfZMic1hcM6jIXNdoexju1+h1jTGOaVeVb1SqSSfUYcCgu3+7fUvK3Ydt0XXQ5rlTiXM4TWyi0WTGuTtjhFCCqa7rcgTP/fOCiyqPd+9VgQlHUBZUEBFCHJaKOPqco/sfUK3SqJmiIlRugyBthstmQcXdfpM0pRM/+lXFfLen6xEa+NVEw1WPFae6dQsuBSJYeMlEQwlpALS3iVEeaUibVCTOQSmGJAJIMt2n4tmbQQG59MsMtUkvW/9VJOLXLxZWnVwoUjUJo449VM0xr/8jbLF3IFksh7n3X1tb72Gvlof5qGerGgX9ePUS7X9d6mrHyjHwxBqVc3RhoamsPE9PAlaTxixWJo8ysGmEckRCQlbC5QMnOJOK9GmeF84vzwhvPhHc7SeCwH8jTiti0Yn2dynCDMMM/YWLCpUKKWinVNCcR5Yjw9Mh4eSWHGWo9pJNNf/lDOm2kGfMZcrWltfmqZ5Gm+iKC8w2oYOuV4nOsiVqkCiyFlYhSCQRHy2s43qxt4TJWYCiyEciN0VgUZVwSyJaFCK+e3UmHIlZAax4nLuAAtD1NaFbsWBPMV8rvq/zSjw6qIjCYt10q4bZ1wCvPZhtSSUyPDK/pirCIaBcM4J1WOzpH7x5lX784czjPjmNSWQWDTWaiG2IyGnRE6b9kOnt5nOpvb+teEUK2lNDkLKa3LslEacgVJAQrstlu8TQQcxnXMIZP9r/Tsf/njhyJA0Tq+4+XNx3zy8iMO7jXzeOL08I4QJw7jgTE4oumYa2RKSeXEa8fD8UyeC/chcMqFajUTV5SgrgvcNVkWkTbgFsyxDTqBUhvB9jKFuMQsTXDZNBiRBhtWYTxHYkrcPTdYW7F1wjghjSPWqaZC2uUnAYoaTC00UdAtQeu9aoa2kMvUUTMA59OZ83lkmmZOxzPH45nD4cjpdNLST2kZWTvxJ8EJcjUBLxD4YiWekr52jiqKFZfWua9pMUsptTNObSNSxdE5RmLrfFBm/VMVwhAD744HbJ2xdeaZrez2W37N8w/ojeHxGJhxnCtY53GbHdNx5CFVTHV4a+i3t7g+8UFInM5HnLc83L/jdDxomcMIXizZGaSqqF2lNNBrYco31KwhWJ113PSemhPTPCFmAOOo0lOwmOrXTcvaQrapBZEVUmqaDmlZi57u1LJ0OWn7rBVFUvT9DHMuq2prrtDbQq5qtphLJY4juBPSPVL9DdYXXjx/zvYGHh4eOZ9PHA/voAW41jldjKqj1sI5JiTCySzS9P7iy3IsyDhhpzdN18QxjhPTPK2w8zyOVDKhTKQSOIcTxhQ6dbYDKdoSK/IVrZDc/FnaRIRK28BUgM2aSudr616q1KS8g9xUQBf1TTFLRnu5pwvPxBpFSp7EFevfZB33C//kWkFUUR4tW1aslpQWgm77/YoS+UvjU638FiONZP500/pexwWtLetczHFiPr7l3bf/f1jn2ezusOL0M4+P8PAWM07YKdLnzCEEHqYTXb/DdRumw1seysirX/jfjIfneBOZz4/E8yPDZqDbbgjnN8TTO8rjA+ZwYDNlurlSpsJ8Djwezzjv6Kzh/tXnOOP55Md/E7Uahr1/snm/f2jQLThjGmesdXlVTayE0oJRtUPQe6jPonMOK46YKp2xvDuMlAqhFEpozs0lE5MhtwAlzFkF0JJhjJUxZKpXSYFNZ7nZKufCe9s4NE8z16WUdBwrh7mqKS0aQS17hREuCW8VjJQ1uF4OYw22M4RReTZh7rR8GKPONWdbE7WosZ80jmLNhKTrf0H1pDo/MOZKyJVXb4+cpsi3v3jg8Rj4pc/P7RwsvYl0pvDxy1u6zvPufqLWSu8ct7uO57cDWxPUa8xoJ2Xf9xjvydJDhhkN9mwuJKME5jgFEMe3PvqEWg0pFvpy5v54JHSZr5gkfR/HD0WAYsTgrafkyjRGpikxT5k56KYpaBePEcFZtc2uhWbIV0hpEf1p7V1fEf1pnRhX9ZUW31OR1Wzv+niSLLR4RX9nyQx4UgOvpbQBmpmnxLBZ7OKbdLlZxMsuh9ZsHSymImvL8+UcSm6LPxr564RD2wRDYA6BEAMxxhU2X3okltbO2mT4v3pczkk5JsonSSldarrv7bZrwHMN91Kv2PRLgKdt1ksb8HLkHDmfDniJ9CZx+8ENH+43fPpijxdBeOAYK+egfJLHxyNffvmWL9++Y3Adm77nkxcfKnxvNZhbXEdjylSjmhsxJGgbzlJTWcpwppH+BNt4J1ozd8bgrVEpfAPV1CvBucvY0SSqGegtZTMpa2CyLLxqa08L4C4ZnFQl3C3LuqIrGkDlCmMq5GqwoeBI2OOE1BOCR9jq29Yt1Xhub3dsNh37mw1rBNA2knkeSSkyno+klBgn1dYouam01YqxAWMdw15bmX2/oeSscHSniEKuSYOvkJCckKRoUcyZZr+rqsFivjLKVoi8yQjVli0uaIYxet9ta/ktqDZLaXPzMsJYfwdBjUHb2FpQzcVwbRnTCydF1UKbLX3zu9JT0J3ywo3QjWTp6DG2tG6Y0spTTaWWa07RN0FQ3uez6fOvtTCdHhkP95zv39Jvtmw2e1IIVAzxdCadz9SYMUVJz7YWFXOrYFxHLpkQJo7vvoQ805tIDhMSIzVGLQnPI3E8UuaRGgK2VjwVb3XDnubcSs6W+Xzm9PjIeDpifU+/u2GtT3/d9Qp4Z1VEbIGgWnZ+iSwLNPTSWtV7kUrjFxqGzlJqpffa/TfFRC6KLJ5oKElDBuegQWzMELOuScYIXdM92Q5euTBrizKX/YHGXSmVMVqmeEHrLvGXXC61wXYXtPvqssVgjFcOTwuCEVF5CIFsLFiPWEcoBokwjZMGXWqUgxRLLdoZ+niOnObE22PgOAU+f3VgnDIpo+hI3zFYoTeZbW/pnOFsFHX0ppJi5O27M6kXcu+pVVQ4z3ZKOkOLpTonGx2glZekXWOcVfDNi6VzQi+eYL/fMf70+OEIUIylcz1xzhzqyOEwEabAPDYiplVynrO6Qfc9lGSpBWKcteVu7VJhmRssG2spuS1s5ckCRtWJqWvVBcqt629eduJrxHcJThbNFBY78JiZzhHXQbfvwYm2sLaNz5qni5NzHuc8taqRora98SQ6WmTvjaiD8dCr+uA8T0zT8nVFkr1SsFoGorbB8rUL6aJDknMioGUdlX9vwdiabS73slwCNmlGjLWSi7Y/q3tpsyjQLrsnR4qBw+MbNq4iHl48+1F+7acf8+s+uMXUQooWOU58OY6cTxOvXr3lF37hO3zn81e8uLnjbn/DT/7Yr129LkqFOUTm0ETqjHqpzEbVOTfb1lEjCydGoVpteXW6sIluOs5aOgd9LyQRcjNnKhhKtRck6mkNAFD/odWnCJoG7eUoLfiQ2sTOViTAYDE4tP04UzkGsEZN722K2JQxc8VMGQkGs5vw9JjNnhcvbhGrpLpVIrw9i8PjO6Zp5FWKpFw4niamOXA+T6unkzMeax13Lwp933P3TO/FZujx3tN1nlQjqUQ4BeaYOQVIpRDLosejwZWVJ0O3BVyLv84FSFvIwgbdnLyzqlZcC5VMXgKGuiQRLHu5mr1RW8v+YiGnA1JaErNI3RuzBMgVawXnTBs3OkEuthaqQ1LbxmSschWMVd2KBckEkOYgfolvlmjnex9PuJVrRi+UAqeHtxzevOLx1efsnz3n5vkH5HFkniLT4yPp8RFCxuTKYETb0eegG5DryfnIPAbuv/gl4mHLxkTVegFqiKQ5EE4HwuEtcj5RpwlfoRPoHYxVSZi5qHrpeDhi5Q3Hh3dY33Hz4oO2Viz6N08PI0LXWTpn1U6DxZNMy1EV9bWRRsq01mh5oygfzXvtujFi2HSudSg25eUEc0SfY7tzU1YPrMWYFLQjbugs+03H7bbHObeKQV64JPrgYsrMqXKeCudQviIwuJKq299pKPz7K6gYh7EdYjJikmqaADHMDf6z2KHD+o6YDSVVHt+eKCnSO4ezjsF7bLaECq8fA++OE++OgcN54tu/9I5chGF3y857bm83bJ1jsInbjcOJ4WwKmYq1mTglvvv5xHS7Z77p6TptNMmuxzij9ElUtoJSkbYw1aKJW63C+XjGO8ew6Rk6YTf0jP57d3J93fFDEaA44xj6LcZ05CLMoRIj3Dz7QOFg23M4zJyOR7xUNh1I56nV8PgQsWIYho48FFyXKVE1AGCJF9FSyrKxiG2li6JiPcAakrTJd6Gh1Kul5KocU6+DoLIGADFWpjFjDzOb7Hm226nIj1syb9p50bRADHNQETDnOyiVUmNDLwo5R0qObDaW/c7z7LbH28TD/WvOU+I8JUJKpFI1O7GyGvblUsitfGOb4EtBO1pqywznEJjmwBgCnS/Ncr5eAhRYSby6aeiFL/O5tEBNF/GqUvambcRN+OoJclQKxInNZsOLuy0vXzzjxYtnxKyL6HmamUOEkjkfjrz67mc8vHnH+HgkuoHkO+bzRC6RX/zF7/B4eODtmzeEeSLGwOA94ppEqFm8g/QZ5bbZmEZALla1TWxtstatnmwNanpWI+LbJtcEN1RXRcWUVtE6o9GYN3Z9tjbna44soVTmXEnmygxQdHz5Vi6ILYhZ9KvOoWJsxRWQPMNcIRrkMOKSwWz2dM+e47qBYXeLOIexizy5tqfHEBRtm2fG88Q4zRxPZ1JUHlPXaSCyu93R956ud7oBlYxIIdWJmGdtK84TKU+UEqk1sRhkVlqSbPjKzqXBhazQUWk8IBHlCVhDcx9h7YAQq11BLJWhql4sgiC29bdJ1dLjdQnTKhHTWdPQSIXlVTh08aJqG+zCPTEX1KfUJVlezpdGQNDWcalVSzpm8bS+eu2vcFzUUN+32zBQCiUEHr/z8xw+/zbldE/tDJzv9YZWoY4HyjwjSe0ENkbYCGwoGIEkjlI7Mon54Q32/IYaHvFdz/7uJfZ8wLSyjhxOlDlSU1ZDB1PZd9rGfUyAOMT1hPnEeBKm4xs2246Sf1SfhXFXuNbVemYMm67TcpLvUJZzVKmGUpqivHawLDojlaXLTAPHvtObfrPpKKXijkZLnAVKXgj+mogmtP08paLaOb6y33Z89HzLdtPhmjSBqmnTyuRZSzimBSixMiswePVcKk3hUv++PN4l8H1POEtDZIvzPcZ45pQ0eI8REYeI43iOjI8PnKbAHDKPDwdKKfRdh3eWTddrIhozD8eZcc7YbkvMDmN7KrWV7zPWFHoHG2+oeaZUeHGrWkh+0zHPkeNh5jBWxhTph0znoe/Vp6uzltTey6AK1slcGitSKjw8PNA5R8+ewXjM0D2pPnyT44ciQDFi8bZHUJJiTJWUhe3uGZ33WONJ8YCpB7wB6QRrPbVazgdLkkrfQ+wL1utgJC3YnIYWumg3SFWa83DLvq6Wm7bg1isU5oKjrNnck3rmBW9BDClWwlwwp9IWSG27c1a+Ur9cFshctV1sidS1TNLg8ZKoNdN3js3g2G89RgrH4z1TMEwBYmrtbyvR75Ll1AaXC1X1Orh0zy223iEq0x4BZ/U+LSWJy0p9mcRPWpEBJcTqz6wRKNpma+0l01yPWiAFervjdrfl7mbPzc0N6f4NU4jMKaqfUIV5HIlv3nJ+PBDOI3k/k8PAPE3MYeKLV19yOh05HB7XzcpbJdwtzMnr9uqSFSEyovUG1bq59IcYu2hkACk32LO0co0uXNIgft2U9brF1OY1c7lQg30SoKSq3jtLC+V6V5uT7YJGCaJdVBVmCqYIKWdIlTon/bMbsVjs5shWoNvs6HyH0OkCWtTKNMe4mrWFOTDPM/M0MZ5H9ZeJkTQkuq4jZ3VAtr7px5RCyYlUlKSeUiCVmVzU+G0NTtYx0YKUr5nftQV/pQUTFUX6lg467bJpwYuSSvR1DZ1b0UqR9f4viUFZ/ZEW7xVFsuUq6zUCq1Hmol/SOBDSkLZlFq/zW9rAlSbs156ZaQjKqoW0MnS/5sLfvw9XtduVbNmEGk+vP+P8+jPKeKJuOpgO1KzdUWU6U2PA5IIp0BnoDXSihOmCIYunCITTA05m6vFL2N5gNjtkPMPxAKcTnEdKTNQmp24FNk44Vx02Fe1aimHGSiWeH4jjnlJiC+a+PkAxIvRdR995nHONw9OeTSvxaDnHtDKboiiufc+KwThdtLa9I6SMMSrSt7QR51zXcnUxGqDEWFUvqIPN4Hh+u2Ho/RqcaIlnQXmXIp4Qs1orhEzjn9TVUJqW0OgwWK5SA5uvqAa3tdK6DmMLYcotOchILhgsxzHw5hR4d3/mPAYejhOlVIZhwHvHpk+EcWY8n5lDIRd48WJQ/R3TITW3Dk1du7zVrjfmQK2Vm+0znHf0+57DIXA6Fs5BSFOiT4m+h+fnQM4OerUXoGaMOEXm22KUCtRcOB3PJO+Ig6P0Fg0z/l8coDzcH/nif/wivd/S+YGbmw37mxuqccypEsczphp+/Fsfk7MOtGG4xZiOV8/vOR9mPvcHvuTMq59/12jiSzp3MQa7MMvzk0VJ9/al/VN1M6CFISthFkVdrgfssnG1DTCnSph1JSsmQ42EKbLZej764Bld3qy0BAScF1w2yKytbTk1med6qVV7ZxXtyJl5nnl8PJBSk3SWgVQ9MaW28V2rxLZzW0s0spa3Vka7aNW/1ERMEbOgeI28WxfUqV4gUtHajrbsrhutrAsB1BUp8uvCfrVxizrmbhoU65xBDPTbPdb1fOtHhbs5sT0FoliCeKZpizUZK4H5/I7/9l//MzEnHh8fyDmpYSEONVMsiiil0jYk27g89YKgGEVOUkoNAVm6N1QF0wmQAzlBN9whVtj4jZYClk24atZojTqJ0NQpdTHUUsT1EUthyoVTUf+anbO60Vztbbma1rmgnxNLUU4GlVyVO5LHSg0zc86I75jPj9hu4HBzhziP6QZCUTXaNw8PnMeJ12/eME0zD/cP6nM0zeSkQUY0FWri/uE1czxS7KzjtyZqSVqyqxokpzK3Fv2s5YOqy32p6tPzJG5vY9w4i1SLFYttpNuUItTaOFgozLxs3out/NLGvUzStXx1xUyppo3BtpkY9aiyTgOgWsA5g7NK3FSdFZ3rNK2OJSlgKT80mHstCYioAR6qrmxbFm0Wr6nGS/nmh87VkibK9Mjps59jfPVtzPQAZ0s63Kv4WCqkaSbHrFo7QG1cH6kFW7MqfnaCtw7CW1I+U8d7nEmU+S1pzEwuMR5fM58fIEWkVDyCz8K+CmMWhlzo27ytpVDIjOdHhtMNYTzjBsHYnrX+dnWUUpimiZgy3dBDMVCa9xF6j4w6dWot0LagxqofljE6lpyD/aYnZRg6TymxSRfoQJhTotRK17dyHpmN93zwbOB2v2Ez9Ah13Sdqc0lWZWazrgMPp8hxTIS4Ixc1Eb2knK0b7wlIJmuien2kXAmxYBfekzN4kVWV+ThGvrw/8523Z8YpEWJmjIqMlWSRVHk8nckpkaOqb4uzjDGjwpEebx29N9xtB263A15mKBHfqdp2JmPEMvge93zLsP+A73zxli9e34O3YIT7x8DcJco+tYSrYKVg0WqCrYVTKtC4ZGIt1jnGKfDFF2+ZP/wYbr75KP+hCFBCSDzcn9gM0HeVm9stxlndhHNhHoOqtG6GNbDYbHcY05FipncdxzeRw7B0bNQnabtZZK6tQnd50WyAFS0B1qxb/ypr1ga0oOEyiJ8M1Gsp+aZtQjHUbJR4WorKJmMuAQot2zM0T4hFxXUNmViidinS4PrI+TRq4GA7sJUipdl0L+d2WcCFRcp74ebI5X2fOLpq8FWKrisKLVeV9AcW40IVKGsBSykXhKW9j7TXLu/7hO9zdd+cXL6WTirrVeXy5u4GHzNsooonFeE0bVEFSGXzPz6+Vc2VlFhMyvQ+VlIMa8Coz9GwyGeXsnCVCrUuvJHW0SX6WoOun4JqE0gj+zpnyVXF3tbh1YJAY5ZsvzSE4KtL2VKmiKXijJBFBcecubRf1qKL4BKgSBZSbXLuy/jKSUt3nCDMZCqm60hxBucR3xOqEKtwfDxyngPn42lFT1JSUzLlhVRKFpKpzPNIlYw7GDBVu7Oad4pCQdqtswahXAX31yDi9bNeyihVmniXokS5WVGsgfT1ZteeA2bpxNNxq8NoKZFcRjiNLHsZ4y03aSW5BTmxdlGPvZzX4u9CVadqGsFRluSjvaG2zy6ibouXVLOGKPX7VpL9aomH1kI+M58eCOcDpEhNjTOSlRgasyYsxlhKI4FX09DhWjC1NHK3UEIkxxlSIKWZlM4wO6pzzPNIiLNm9rURjavgMLgqmKLcKAVBNXBLMZDiTE4Rk3ObG19Fyio0L7CCsVb1QcoSvF0lTg1SrbR2XzQ4kcZ5rqUJjrmEt2YVU1zW3tZgtX6qlYr3wm6jDr3GqDN2XTSbGj+o1Kpt86WSSmWORf1nWqJmW6lPlaKvghRg6Yb4uqdcWmKqAZd29SAV2wLkKUTOc+Q4RkIsrUNNrzvl1kEZY5sDOnhN48hILdoYYpSfs+k9vfe4GhWNcq4FRqbNEcFZz67f4t4+Nm6YoxZDiAVDJUYN0hUp1/XNUHDoOJJa6boO772i+yUxhvh/jhfP/z+OWgwleZIBIfLl6y9xXhDx1Az5VLjZbNg8d/jO4rzFmBkxmY8+Ghh3hscvDe+2UCRTxYBYqCqv7Tt12dzttqSUOBxO1FSeQNK1PWDrFEao0DQZ8qXU83Uz88mFCF23ZbvZ8NG39vRDZTq/5fEh8vatsLfPuFmTrYpIwNrA0A+kXAgpaCmmdZBApYZETonDSe2wH+/P7G9uePlhxHZbxKpzr+6Vy/ahJQzRnAGzXFCBdYUBvLfc3mzYbjq8MwhKCqRe/CTW0lPLYK5h6qsL11CoLojVUt+nbRSXVy4TLsfIeDwwns9M88T+7iXeO24+fKYz3VtStcRqeTiMnM8zX3z2Gcfjkc8/+4w5CtJv8d7T9z3eqYPy48M7haeNOlYP/UAIE+eYGk9Gz0NaFnGB83Ut0y4pYYpKLpYSocR2XaZ9XQWPInS9blYpXTxBhBnC5br1NyrHmJlLQYxw48D3Qif6WJzV++41vlVL9VJbu69cem5LoaSJKkKcZzCGub9v2ama/mWEMEZSzJTTRI0JCTOSM6TUNgMoUblDx8MRO1nmNCIOxOk4NFYV8o3RDjoN4BbtnIZ+tA4deW9+CIKzKmzlnFfUJleIQcs+Ui8lwIa0SVPJMGJUDKaYNheaWWCDsATwzjVegEWkUiWDtYjzjVCS8Z3Qe4N36vOjpG3NcDXggVJN042xVLFY3+ML9NGQ20bXwhasbdyGFjBhwdjvX8Hq/SUkpcA8j9wfHxlPJ7piydEQRjhHYYoGxw22d+y7W0rOBBOIcSTKA5DxdWbvevpOCA+RaQ7YAnMIxHdvKI9Hin1LmSbqHLG5V90RPCeB7FtSlXWtcMYgtsd5R54nwngmjies63AsOGG5Kn9oAB5jIqVKxenmaTtc16uMvnPUZPU3ayZEFUXru147NBFtR5dC31mGwXOz7SkVxhCZcyVmEG+hCmMFbyp3e8MHdwM//uFepf9PMym2TkKDSgo4R8qVMRRiSmqTkYRIxzAMdHbAdRtyKYzT3CQWlmL/1eJV+QpylKJ2x/Wdquhuhg2lwpQDpzHxnTdveXuuTBkVSKs6bgWQnKg5UuaRzWbDbn/L6axE9uk0Iwhb57jZbvi/fs3HbDrDrhdMFUz1bIdOycbVQYHHxxGxEfEZZzJ3Nx2ddVhz0ZXJtQVhOWGlUiRhqPiqnmS7zcBv/ORHyDkxH99Sqyp2P6lXf4PjhyJAcc6xtVust1hnmMNMiJW+MwgW6zpELDEp49u6qtl0VWjOSFkFgFg0UKCBshXt9BKGnSUlmKOBWTeUJQte/3uFmFyDfk+OumzJV5lcvWQKxgjd4PF9IcWiplxzJnflPR2URK1RiZeLfvdVNihru7S2MNdSdK80nn5zxvqKuLiWKpYzElmg7Z55DpQSri6zQhXlTRih7z3et3rt2nnyFCrS7qKnmirtR+0cL8HJ6hdTaUjF099puIZ2C4WgMtgiuKGn63tMV9R12FsyltRUGbfbSEkzm03HNJ4Y54hMFe86hqHXDg0jzGeHlIy3ahC2XPeC9qzuyWtJ4enzFVmk9nVjXngQNJh3UQ69iHpJk7RWDos+u0KNT1nvpt3HXCtkOEXlv2yNYbAwNB8e0+69aoBIy+ZkHW8LpXO1ok/auVSpVKNtU1oCMJiYcbkytJJMcUISg22+RLloYF5BW9mNlr0MSweMrEJ165QQFEFrC936fNc49H00ZCEql6Y9UZ7iS0u8vJYmL1+aZbeOq0Vyfgkm6zXnxFxQPK3hqCkiippYK6uI3SLQR4Pw9fQXGfQLqrpk+PVqXl+MLi8Oxu9banyzQ71YUkmEnAglY60lioVqOefKKRY6PE6UVEkpKnAYDcNuwnmBGgGPYLUkHis1VGzJTH6mWsAJthisGahmCxI4xspY4CQwSSW11KbWJoEg6rKrZn+KqMl65r/CVVXaM7Ctw8yuGjSsT19AVGahNVpd7n97c2PMKs+gz31BZJbSGgydytk7q+XkWpti7fLszRJG1TV5iLmQUkMwrpCEy8hYusNkHfTrbvB10FEt5LyMA226mJJqsxzOM3Mw5GLWsr1tY1F14HSxsQ0tWsN0qTgDz5/tuNtvuLvZYClq/icGaxymGb2qXk4lzSo1ISXhLWx7JSRbox1Wzio6KVRVxK00Xpi22JXiqc20sxrVi7Ki4yBauXZp+b6PH4oA5e5mz/DxjytpKWW+8/kvMoeJb318x2bY8eKDD0hz5P7xyG4D2wS+yxgDoZ6YzpkyF2ookLQebiuA9sGbruB3mZc/YsnJYPuBw30gBo0KBRq/Ql12qRphVl1Z1/Ns24D2sKM1TVUX9G2AF4WLCbhNwfYwjsI8CdNB2O7lSYCS8pGUD1jbt4X+oh0iRtsuoazlgZQK83hmCoUQK1gHxnI6nZsEvW6azhm2m4Fndzvevn3HmzfvyLltzKWC0cnoveHubstu29N5u2qmXIIzfT9rDFKuicSsYlf677oGKEqYpW1YzbPnOtNCSNUxzomHw4lcwXcduxcv2Gy30OcmwZhbGcax2e+pWfjgw+eEeebFyzvuH478/Le/wBpL1/XYlpGXsSdY6BeDwSZep9ehmXkplRgjXecad6Y03oFOVKHQWajeEA0UU8krauLaLmmuvDYUXXPOr/cjp8OTCd1ZYXDCKWSmXDnOkXtrOHSWvTfsnWGzBCnt3ia0dj5fxVK+BaFd4/ykhiqknClAkhY4IexE2AL7HkovhG1PLJUpV8ZcmVNlTFnN2YS2Res4MqLKlM5oJ9YCOJU2hpR0zFpy5T2S8HIYawgxMU+hldj0tUYuAcGy60lTItUuKX1d9Y2wGxMWg3WGxRnXWBBTcc43nRP9PUVntFPK94qeLCR1MY3vVWUVgitVqCKkaihF29ZzFaxot1aul7KQb2jAQhosJTddne8zSLmCUGqtzGnmnEbOZWaWQrfZkLsNIwNvUuJdSAx50CDlZkvnDLuP4OWLA7+m87i+B06k7JiT5/WkXNh37xKYjH840m1gs+15cfeC7XbHw7xj4szrhzfaNl4rgcpoCmMphAi9dTjx1JQocaakmVpiW+dYIrsnl7XozuRcsc6B6TB2UOTFO0oypFza3PE42+H9oNYBWdvVVZskEGLCVB3LxlhsyVi0M1CDwkTvDS9uera9J4bakjJht1VEdbvpEIE5ZUoGaiZl1dc6TYpQkWbELmV/WW0MLoj0r/xcF85aTomUC67fMCf47CHy6mHiszcnIh2JDmO0A6xzSg3Zdh21OEI0JBxz1sDB/9/s/Umsbel6lgs+fznGmHOuahcRcSLOsX18bUj7FoaETFICKYWuJWw6VB1LNBBIuGUkBAiBBEJISCCgAaYBEh2DBF2QUEpOkLJBI30RNhi4aePyVHFi74hdrGIWo/irbHz/GHOuvXfEiQgfkzcPHqEde6+15ppzFH/xfe/3fu9rFes2c7Zq+L/8zt/GqvU0TtPvdtxd7yVwMF72HWNo2hZSZoixtqFFLhrNyjRYK35wbSPq3KKMq7HGkNJEShPjNApROygimik8xzeG8zPPShvWuuFZ67n7dCP83vEdEaAYU9uEDxPkiLMNoGmaTpxvET2FKSWabBYSotUKlCXbhDMBq52IYJXjwqmUwjUGvzI0F4qcoIuOEDNuG8hRLSI199qIl/RqkWYDlgTyZHIe0RP5MoGKaJMrCQ7iBOM+Ef2pNkYBFVEqAgGlxSxNNvuaN6siba9WL/VxlMCEIUZyzGRUrf3OAYXCOct6veKdtx+TYmS33TGMImhHvSfWarx3rFYN3otPw3yF5eRfJ3nNvam6cALm33gD4pRnDYwTWFQpjbIeVF5QgFkQTp6XkXRKaygGrZyU64wEgsY2PHj0Ns6vGQZZ2MrMFSmFsXEY0uJWm2Nc5O2zrsFT5ZWUqhEjQn4KlefSWCVbaggcvYWUUljnqCzsyrtRJxvsfBPza5v1fHd0DUBSyYwpczOJ99EUC2srrbeNqXweJZtjrFiggRqIUUtVkmmJSqVkv1YVIpmEIpZKqpwzt/q8vFG1zbZQNIRSyEZVm3q1oD3UYCSVgspqeY65lpoW8KTOk1epojPqILoYrpKUqwTbXI4sR07BCR7J8fYdS2kgPLKi5vOXF5uqb2KdmLDlnCoCNvNPavAi2BBzL8cM4hcl2W0uM8fhOAPk/I8oyWyiqfUpEsBnO05en3OSsqqW9l7jHaVdgXf4ZOiUo80KpzK6bVBWY5zGZVhdnhMzTGliPx7QwXLTT+z6xIsetDOs2oYVLUZ1HIonR8uelkHBDVuGEtnGQIiFKUSe3+3onr9EPzwXxGNx0p6FbApvumBdzT3btqlBpwTxx7LosTQK1TCvBqmzrYZ4eQmyl3IhFhm7Ril8FdibCw3igGy4Om9pvV1K8zErETlE3h8FGZnfuYhTfMwabVuc8ijXgDZ1Ta9YWh2YgsHN/Z7VyXpZqI5jXGtFiLKWjOPEFAXhc1rROYtHE5ViVj84awqN1Vyet9KtF1u2feT2EDjrPHZjeXTmON80vPX4AqMV/f4gytxhwhppCnDWYZ0QZattlWjMKFsTrUTTit9U4yTxHvtIJhMyhBAI01gRskLbNEQc/TDRR8ihoNeW83P32nr2aY/viADFWoPtGvphIsbIqjsDpThfX9RILzHFSB8jbbKUoiqzWfrPLZnOK7wZ0Ll2FSyguKJda9aXhs3bShT7XEsuhf1dIPSFOCGQpJoFs+fNuIK9M+ESWaCOmzks3S7zC1QEXdAmoo0mBZhSZvcycNFkeHi8bq0jSo+QDihtaVorpLhwVLVyrpYRjNTkla0M8SgmfiEWwlSqhkRlxjeeB1eX/Lbv/15yjNy8vCaGkVgi1JJE0zjWq5aryzNWK7+UlSRImJeRWWlw5qVQofTjNZyCtuXed44uyPdQUW0xfg16JJVpWYxSklY6XWx9BgqlPdBgjAQFxoMvhfeaM8b+wKZd0+/33N3eUKpCah46RiMCfqlItpSLsOFjjsdRoRQpSatlynbhFOgK9ltVKLrQ50gmCpHWSNBMUQuw9hrpUZUleDg9MlL/nSHnAhxi5uWYabWmNZpLr+ms5ryRxQwE2chobCXyGcSzSxm5z+LcXeoCLAFNoBBKZihiXZ+kyQUzbxi6bnKAcYZIISghRhaDcO6QIZiqMN9pO/HxWk8B/7IYSd573FrjtEZ7Q6jaKzGUxWsqFWlv1Oo4v5SqWfpCotUV5p87QYpwJuo8t9ZKu2bryTkyDAFrofEiK26toSrykIo9BlYKCU6rFlIEUlY1SLlfbpLTOYoaKuWOhNvXrvqTjldmShLRLO06TLvBbRqKX5G7lo3X+ARebTCKyqPRmMbSuIZzMtcvrznc3LDfBlKGb94e2B4mnu8UTet4dH5O0edYd8lN6jCjo1cbeuP4UO04lIHrKZCGROxHzJNnbPcDjflurFGUko7IcJnFMOdA43hYa3j48Irz87NaPRP6JcqglRExxIr6aS1l+9lAMsREDIkxZOlaisI3CaIlhlEK56VFfBIFAs5Xhs3K895bGxFszDCFwhSQzkgjBRGFEM1TkTJ1zIpQNK49x5gVylgKiqESx2sBSRChOq+oNhkhZ1S+v+XOZb+cIyFlwnZPzAoHrKzh8aYhIkaEgkgWLltYNYp3v3BOUZYha77x9Jr9ds9bjy95cHnBl794xcVZxxffe0zfj/zq3Q1jGBjHAe8Uylva1osXV+2KIxeMsbS+wehC1JnN2tbyjiXFRL8L1WMtcjgc6Pt91aWxvPXojKwcH928oB8mDvse9d4l71w+5lSE8rMc3xEBSj+MDM9fMk4RpTKbdYOxFlcnSH84kFKiaRXWAyaRivSyZ2CaROJXNhwW/kaq6rK+WdF0Dr92Iolue1Sb6c4tJcvGmE/q6QrZ6IuSxWrJEjkiB2oZwogQkRFRrW7t2Zx5ySZjQSVFyYWpD6Rwv4qndcHoTEwjSuUKHUu/WwkTKQcpPVCk81IrtJ0FxTQqyWK+kG6Wo2BqmadrG9rGs9Vh4WHM2U636uhWjfhF5Llr5eT8apYqTPrM3A1TZna+KlXQrBy7d5ZW7mO78emhlEG5FhFpEpG4/aFnvz+A0jTuDF0MyjVoPFq1UKygVEoCRm01vjVcPXpI13qcLoz9gWkYGFuLLpYcpOylTmBiXdEptXRzUMvMuXor1Y1cC4cHpbBBU5SWdllrcNbJ5pWOLqemlnqWBVwXir7PQdmHzN2U0WUWwTt2n6RSGFLmkCVLc8XgEBKg0hKY2FpyccikNxUxS9X0rFJbBD0EKFIe0lVvRDbgWkdW0rZcimgQGaUpRot/ma6t51UnY0HyT3g78yZlqyjcDIkn7gcwakZitMZZJ0RbaxhR5Lox5yKdTZra1aXnzjN5FqmON6iqyDEsAcMsNz+jXdYgPBxvsE7ReIH8ldbSIVVU5fWoI99YHxGVXNGilOf5qd8YoMiUnzvAXgnaPtWhTv6lQVmKXpFMZNAeZ1tcJY+2KFHZRcirAGMsjNEwRUPOUpYQMT3RV/Ftx1vrRzRdx9WDB3jnsb7FWpF/b4y0fz968JB+CjTrc4b9gcPNlpAjL25u2e7vOFtbZtPM4304QY1OExUF3oLVmZwDKRl00qQ4kVKUbL2SaHNJaB3k6o1ZRCGNlSvNylKUlGx8NmRviWQpRVZWVkHGddM5Gmtx1jJNhSmUpdOqnyQyn7KMwc3K4doV6+JQ3Tsody5ISc4cDltSDIz9IO38SaTrC0qcibUBbbHO3UvQ5nDGWgdayLVGKy5WjtYVGuuIBSIiyKhVodMZbzM6TYQS6SeRbWi85a2rDe++fcl7b51L6d0qRpWXtQXE1LRtHM7IujDGQA7STFGilENl7wjEIG3DaQqiIzMjYrNQnNNsVh1N47h82JGV5r2w4u5O88E0YMzSC/pZBznwHRKgjMPIi5c34mppDevVBu89BkUMkWHYobWmaR3WFZTOpBJRSYmw25SJKVYX3ZNjFu/yBt85/MqSQyaaCdVAu7GEPhNGjppudbXRWi8Syq9myXMGzrLQZZTKEqCsPOtNK/TclEUNsxRCP5Hi/QzT6ELShcKEUnOHgQYMIYYqSjS3O0vWPPuJmNrKBzM19vQcC9ZoVl1D2zZ459HqUIMvWbCbrqFdNXSdEEyLEAqWdlaYN5hZuKsqAZwQXxXS/pzn81NzW/GRLCuT+eTctJYAJU3kpBinwOHQczgchEy32lSuQYOioSABipK0DBGAsxht8VeRwRlsiexU5pADnbeQDH3JqCzmixJwVn6Dnr1YSn3oM05WOfv1mq2p/Jtc6XPWCCnNuqqKmSEpMmLOp9Tcei2bbX5F478Phe2URTVWgOQaoIi0eypCWMwZmqJIRYJDq8FYVbMvhS3gmDlWtfxYIRldZPTMX7sCGmkjzUiHu66Z5FT9S2ZND2vN0Vm5iFllLgVpHpqD92MpT4iicynvGLy/ulfPAa61QpRMyQhnRilKTtJ6neQ6ZeTLIn4sqaSTeSiCXVrV8c885lg4M6DQXjRPvNdzMa7OI6ptQQ1OqC271KAlSzFgbj199c8yu2qZZ/7W3Dv32Ws9lXyLoeiOpCOT9mjTsPIer3Ulls5/RMejP4yEpInJkJLc9BgCU0woI7Yhlw/fpWlXnJ9f1ouVVnmjFcoqrLNcqUvWMdOMkX27QxfL+PIZ/XbL7rCnH1vEx+go1VCf6it/y7+cEW2NkiM5BWLUpBRJ6ei+HjKVWxJR2mBSLUEqKfsUFEVJYGSNolgJOA+xrqczkV/Vday1rBvPpmuZxixK3pMQYft5DFd14XVjWNkV2Z1hz95FNw8gTuQU2N1qwjhwMFWgMBw/w3cdxnpMsyIjHX4nT5CiNMYZVIE4BLRSrHxD8rBpMlPJxJKwKgvRNUmJWeeJHBVDL00fjbc8vFzx7ltnvPPojLaxhCLWGEsJvQZurXc4rTGUyhMSv6WUC1MuCNVA7odSmhLVklzOc7nxHuc7rq7OWK9bVueOTCGUFd4VXjzXS4DyeY/viAClaTsePnwswmUWDvuBfr8jDoGcEmMY8I2nbU21e49Y3com5Rw0itVK07YDxlh5CKJmLmWP6g+RykQqI1kf8OuW7vGKNBZSKPQhk5LY0SsKqCN34qiJUk94rpkXYWInMtqCbaFZG7qNlSw0V9+XmDgME9P4Cg9aNTC75hZHzAqUwVpL0ySU1vT9hCJKacCA8rXjRqtlgRSTPlFuVYp6Xzzr9YrNZsXZ+Yrn1zuUHlFafEnWXcu6bWmcw5lKg9RzEFKOF1o3elXhz7pVVL5B4XgzJFiLU6r99wnvpFvkdNcy1tOcPSAfCmM/cr0dWL244e3HW7FIbzzFd8IZMYCpfhrKHj+rFMCB6bCdYv1ALxol5foZKQ14G8SATlliUYwFlK4W9tWjxShBUywWNROeKxnSKCkJ2CxBSAxbWRz0GVkZolISbBW9cFF0mTtLLEUb+pPh0ufMNpwSho+lsLmOrZUSvkkGp2CMGW8Vmyrbb41ilnB3NRjROc37zwKk6QqqlVqaSeQq8CWbtVWK1mpMUQRjSdoQtCEpeaOSFLoWtWc0Y/6M5amXGaGkBi5IdJVPHrYMC1IODEMixlxLeYi5Zj3PVDUwrDreGZmDatmgi8nzcFw6QmZ+iXMGaxWqco8639S5egyq5pJOiFEa8EsVrlCWhCj9RiWIisr3g43T4OR+sFJOysKfdhG/H8woDdoCNoNJFBUWoqRSM5qkl7Usa/BOk4PBa0tjPNE2xCBE7pIVMRdurnf4NpOzr7eiIN0+CVMTm6kUxpDYbQfGflg6I6VrT2wyPi04VHJi3G4J3kKOkA0kQ06RFBO7fuKwn5gSOAtmoyhJoQp477BasRtECmCMPSjFW1crphgZJstqiowhIQXOQiFhjZY5rzTWebQqOFfoVvK8Z6HDOfDWCoZoGGNAHa5R04Q3co8bH8FpyvoMbSQR8e0G23Rsrh7jmg6/Oefm5o5/9+//wxGt11rsJUr183KSqITxUEXhpNPUaoXXuQo7QsqwHyb2Ae72GYzj8uqCzdqzahReJ0zJ9P1IHPoqrhclKUiRFEb6nYiulWlCl0zXaGI1rJ31f1JITDnStS0UTZymSiZuOTuTvaFbWZxX5NJDSVysE61r2ay/i7bxrFctL5JdYsPPcnxHBCjGGJq2w3tBEbZ3O8ZhYNpVAzydKNlQSHVRUxQaqDVpMQKbvReEMHTaBnwkaQm5zdiMbRQNFt9KZ8AitvaKbsfr4kpL2rRsAtK2VSTT9Rrra2dIrcnnDGlKpPTK+ygLygGGgpgfLqx147C5YHQk1YwRoBgh+gk5sxJaSw0kOOZas4CZb8Qfw1qzlDW0hsY7vLNYbWp9XxbNhfR5ZEDW9z5txuQ+D4cCiPBZjCIhX0qq/ff3s2ptLE3XMsYDYXAchsB2d2AcBkLjSUMv6FOcACe2BHp+JvWJlppra4d2InXt2h1uaKWbQy0CiiQlipZTrN0tWgnHoSIl0rpb76nUqEBrkaqnCnzlQkgjJCeZHzUIUVqywaLEe6g+mdM21PlIRXghMR/zbV0hX62EIRHquBgK1VtJxo5HyLFRIZyT2nIrYlszqXPma8jtmTV+MqJEW2QwoyrSUIpctzKapDVFy2YRy+l9mYOa+gzVTLo9IotzYldOruveoWowkjIxpsWskWWqza3ULD6X6mSaScBYlmRgQTdqmXOWTdda3kkrjbO28nHuB1Clfi8XvRgDViyMRBEuSimyoXEsZZ0SoedzWpaEzxKb3AtO679q50tRRcZ67SLTc3A4oyz1NSiW1lujdLXRkCBeq0rrLIppjBQVmaYk875kchrJOeKtqt1ORswQQ6w+WhUBraWko+bRvcf55isrhRwmcozHoLCIE3tMiSkkphBlHpLrHJZ12VqLMxrGo4u11pp1q/FJYaw0EDRB1vBCoZ+mypGRAERrA7YmHrXRTlyxocxib7mQq52KygGVBjE31KA7kX23zmB9I7SAzSW+O+P84dv4dkWzueDphx+9HrDOiUqRrjWR4JkWJ3pTOyGtKpXErgSpiIkQRMrf2kY4JfV8VImQFXEcSdNYW7zLogJbciROVRKgiOWBsxpKIdYxYrQg45lSjWpnLSBN01hWK8/ZpsP6grGFcYyoEmmdxjvHar2SAYrBjOq/3wBlmjLb24n1StM2ijQV8qTwZoUximZtGOPAs+fP6HxL56VlrltvMEUx5cjt7S2HwwGZ1nOeaiSzDwqS4fHFFUUF1s4w3SmGa8VNp7Aty0IkipfiDvlxxeXqeFM3tkymYJ3Cd2CbjPKJcQyEkBmCmEDFKRHSK09YdYA4iKakiFGhdcHagrC0Net1Q9cV2rbClUUxhcThMJIPE+MUalmhZv1Fc9hP7HcDd3d7vGv5whfe5f0PrlHX+0ro1azbFau2w3uLs+YomMVROZaqUSLBmGyKxihpO61kT6Vk4k/jJBoDIZOqRPqyuJ/cxtXmnItH382LJx3PU+LZXc/h8Iy3Ly8Jh57WFIg9moxpA2YlWgrStz+TWuaMskUhQd766i2arkMzMGzP2H5UmEJgOzkOoRB6QQYE0JG20HnCm1lefW4dNhZlHEZpmhxQITL0z8l5oExnYFYouxYRsaKQ5l8ROqKIsmR+BUkQJ25B06RrYd7shYukjZByg5LF2yJdOH1WDBO0GjpTONPQabgyGqekLbGUQkiJkGHM0KMYgT1S+zZ1zHoUTkk3UNLys8kpYn2mIVerhfo7pnaQqdrFIh5BhXGavaKgxskUNXe7nAxvpSSQmyOPOv7nwEFZ0cBIZGkR5/h+86asta2ltyRonMrSsWMtjbPCU3CSpNTaW0V7ioiGVVv5QhWqSrJJKeMWqbFQyj2jxnoaaNQSNb1a5pnVY4/dKZ/uuJ/rzIRpgfhLkDmelCbag5CLjWi6gCLkSUjQUcutNA5tvZRD9QQqSTtpsXjb0XQrHlyc1WzaME0jKUVWjcdoLe+VCqt1ZBonhv7Anbb0xmJsR1GupiR6Pt1j4Hj8JyBcprbd0DRrjPZQFDFmtruem5dbDoeJccxY19L4lsvzNetVy9m6Fb2jrAnKklTk4aVslJaIL4Z25Vk1lsZbQi0VfeOj26o75KDItcxBsFUzwmaqqKDYSyStWHfnXLgL2vUDXLOmW6+xztG0HcY6mqbFuEY4O75DuwbfdELOVordfrj/vJVwiHIW40NJmkE5S1GRkpKQUkvB5AJaEmdTFCYHGm247Cy2UXhfMHkiDD3XLw9QEtfXd/RDoEwTpmS6xtbyM6ic0Aq62kKcSyZYYUZqbYVfZn1FzTNaKx49Oq9lUY0xkX64qySdgs4Zoy2r9Qa0JhgtpOLEPdHJz3J8RwQoocKMRjusNigM1jisEkTE1FQuhkgySRQslUFrQwqRmCLTNBFiONaF6+6pVJU+TkXezxZWjQefmYxoKWij5o60eSouKMSbgxTFjDbMLH5jwDqNMoAWXYqY8hLF59emNJQy94wIUUwkkKVWaOriblRBZyjYoxIgEedyVWgMpFgz6pr5xBgZhpG7ux0hJpzzGCNaEfP5z5oTWs3XOmeuaikV3MuIVS3/cFyw56w0F4Hu06yPkY9/7iMtIs+83lzQby7p1pekQ+QwHLi+3eEUPLxcC3O/XQuKkgK6iEzzksovK6V0CSgtUvmKjtXmDKsi+bDCjhNBKRIZM0a0kBCOz2G+9vnZzFn1XFqo3jyKSIkDaEuKgyBfdIBk23NwI7tjxS1OWO8KyWicVmQtfAe1ZPW1jl2oAWgRImuRWn2R9L6S5GSclaxo6/vYquMSKwIzURgLjMBQWzVnjM7Vh2GXUytCsp23IcXyZyk9qSP/oChVOSCqzi29jJ1ShBt0ihzNpaBlHM0eO6midfpYHhHQ/liKmsW55JGrmgFKcdEYKXfNPjvz3J0/M1eiq2i2HJGe06BqbhtKr+YhSkjCzArK83XWoOn0dcvD/ZTxyQJIlBPNmCp6F6dIGAM2iqv1pC1lFhusm+OUCjHDmHW1LhgZh4lxmChRHINNKViKON+miAqTSM+XLOXAnNFZpO5zEs0oW6vZGXG3TVqL4zaqemndXwPftCwqrWjaDucbISUrQS1SgpAVKDHTs67DOgkCtG1QtpHOShTaJKyBphUF8RjSgo6tVy3rzjNOgXGKNP5QW4Ornk0NniuTbFEGn5+TKBN72vU53eYtVpuH+HZNs5IAxXcrjLH4psUYV8/Po4zFGMdMgtfm1S23zv9lrNaxvYybGoSmQtawdHxCFRUVXoqj0BhJCmOMTGRKTtL5FsT8U5UinXxGy55VTouF5ZhwLeX/ui9UhMlaTbdq5LlEMQcNISIPWeGWjiuBoPRsH6E//Rh/9fiOCFBub3b8yje+zhffu+Tx4w3rdoNdWfQEKQUO/S05JFRSmOJw2qOU1P12uz3bbc/usGWcxip7DZDE5MtqpjAxDBD7CdcUGqUZCSQGlEkYJ9linrMixGFTlbKgWnlBP9T9/5QEN761rM8cxgln4TBMTEMmiJUrumlR9v7jkoi/UE7cg1Ptd1TOSPaUZRO0RiY8CRpdsK7DmI5VO7LdDkxTJCcJCoZx4KNnz/lP//n/w3pzRrfaYJynabrqbwQxVEn2csyRTkrdFCDVSa6sE7g3CVpEkSwgIyhFiIlpSrAUOeS/nER/4HQ1877l6uotvDacr8948uv/O7sXT/kvv/Q+Z50h5MCjRw/58vocw4hRe9Z2hXUdp9NxOdNqayBePoXLR2+Tzzd0emIcetztgNuP9HEHUyKXRKwtrlWG+NjjCsyKpboqwhIHShhh3JJi5GBeYFcFrzuKMhKTGEfRVoTzAJMjum/vPevWajaNYW2lvFGCLFYpK8aSGbNkzzFlppRJtQQjpaEoQloURqNoNMTi5D2t4CNRwUjhQGaXYchwF8R3pEEccJWDDkVnpKMpUcgxkFSSbBzRmxCZ8HK833WzXtaq6pdyfr4+qnUWaR91J2M85yI+HkUybLTBOENRUQIwLX4fRmtKDPShZyZaq9q5pCrRXRtFMSLhb6zBWUPjpEtnDqULEkAdxsCsBZqqOJy4wUpio7RGOyeEwpSY/U+skRLPECMKhVfSvaFt5T+VPGcwC4KSy7E09dkPgc3zVLj+6Jbdy5d0KrN3jrFtRCRPKWzToLRZTCB3U6YfBm5vb+nvXtLf3rDZtDhvaUJCp4k49STj2L98KeuIMdLhpNQyh4d+IhdFxJNLIuWE292ip5F1jqxIWNKCnn7SYZ3j0TvvstmIInjKmpwUxZyBe0B3lWhiwHtP4z3ZrRlVQ0lNLesUztag2gmVWqZR89F1qMJmhtVqxdX5isMwMYyR1XqQ0lECF4s0FdQkZh5Tki3K1La+wV884uG7P8Dj7/qfWa2v8H41R+NLsM1JSe/YuVlDgDfkqkUbirZYIzL+YX+QrrMs5GfvGulgipFiHQVTA+CCShlCJI0HrClsbENOE4dekbzYKRjTYPREng6oknBK03hP23ZMQ09KkT4EaW5IsX6uIsVR9oMoKOj55SXdquHy6pJx6Hnx4jlhnIhxwjgvjQerBrRbiORhTCStEEjo843w74gARZyAMyFEpjFgmHAm01ZiZA4ZnTWt6TDFkEJhGgKjHmVZ0grrLcZVtl1dSq0z2MbQrRxtJwqSORfZoABlNatNRwmK/bOeNCbIJ5kN8+BUSyQMnGTwEpxoUzstXHUejkU6i6aMPKITqeR71338o05QjVPYONfMeoaDT/dl5yx0hRCkzjwOE5Bln1SJcRowk8M4h28cm/M10xTxzpLSRJjEQE6rUsmIcpKLbD0zrB2XibtkozGRsjyzkFKFxudtQU405zmjOT0U2lh8u2a9iaw2DwjDSH/bk3Pg+fUO6ztCSBQTKVVo7R6eMz/iOctdkBQL1mNKxLcrCopmyPgpSbadpHyWU+0wqmRj5g6FmiUfwY8steQ4UuIg2U8cKKEnTz3KiqaIqAdLh5Ci8iFe6eIxCpxS1OYdsYoqommji0CuQcnXs29GzNUsrF5yRsoRKDiYvBCQlZIWYgliFAOFAZayxZxpDZUDpU1ZeBeTkk6yVApJyfAvSk646GPZWZXKgc0idliMlAoXDErJ5n9/ITtyP0r9N8ucqidWkZoimvXkei2zeq2ZSz11dCklqIk1R8n5eYQd59NMihXTRcmuZeyZulHP/SClzmdV+RwahZ4WRcPqKTXTw9XCGVmGYTl283yrYw6lTm7P8gOlFFkVIXCqQh/VEqCIer9hrEJm/ZQYp5GYJimXLQp+umoPFWKoD8zNqrdFbAuUkIlLzpQ4yfuFnpQjIUbUdEDHSVySZ9FCqi5TKRVdPU0U5NBK03YtvnFCYanIYuMNbWMZrJQslUpApIhnB0Y7TN2w0VVrJYn+z/xsjBGejTEWrRNaZ7yzgoClTKwllFl+QSQQ9HFcANZY2tUFzeqcptvgfItx/vhgXrkiWXJfe2KvHbP2kDr5rDLDKVrmtUoK1BHZyVkkAKrEkDxjpTBKkNKUsgR4INoqaTZGrdYuWoJ0bUWYUMjNCOVAVa+sDEXlymcU8U5j7PF+5fnu6OMfJV1taVn7a8nbiY7N5zm+IwIUVWSpC0Nkvxs4HAas0bxzfonKhTxGnLF07QNCioRD4u5mRxgnurXFt5715Yr2eV7gKIWmXTnaM8fjt1ecPxQZ8hAzhzERUdjW8da7DygP19w8/Sph2MNIRTM00skjD2omCwKyfCoh1ymjME5hG4NvLSVDGBLDNhCmQqN8zVryaxG4LPYiHieETCdll6oxMmfUuRSJgphpc9IO2XSGrpOOITcoYupROdFYjfWZIRxggERic97iO0e/H6BkxnHPfnfH7c0dMbRCZqtiRdLCrHG+ASCVIDV/75cFf5pEVO9w6EVpVHtUXWhK3TVlImROScYSfHm61RWr9pzx0GNsx6+/fMHdfsuvv/+MUAxf/r4JT6AxQdrHZyW6+c9pcAKgnWRDuQUyfn2Bsp5VkJbarjmQqC7CqVYZqtbJUbe/SGeOPpKm03gg9gfScAcmgLuVTF1ZVJsxvpCTkwDCryppW6PtfQM5pxTNzNMotWOlomZd1oxZcVAQ1FyakfKE0wo7c2+KdF5EIOeANYqDL6AV2UJQNUhRUjKeEGQu5sxUBfGcLexg4VTMQUmqaroRRdYF7U+crGtsWM2kiVEWyVyE2C3dS7qSK+9vXHPQsDjwlPmzARJqjtaMQhVHXJZ4IbuLkWKR9kyt6oIrhHir9bLpS2kxsygTI90doQYnmYpkOgfaEOf7SN1EjJHyhNKM4ySIkNE1iJ1LPNV8TlE71cpiT/9pj2XLk+hGgkGtMJ1DecN+HIlaTC0N0qkkN14xRtm8+mESaXUm8GBVA52nGMN02DPGxDgEimtQqwaNlfs3BxmIhIEqEykEbq53jFNg6Ac2VrN2GlMCTiUMEV0iOkcxmkxZRGdeObTRnJ9vaBqL0glbn9XlxpDPDPEuMOSBmApFGWIIdH5FZ3X1mMmMaSDmSF9RzlQKTmuc8/WPQ42ig7PqPGpUbA891ihSTlgnqtjz88i1w60o0L7j7MF7rC8e06wuMPrTbp3H8fwmrlHK4jgt1fWKhKPA6Fo2VcQMxCIBv4actSjlpgRFvIRcbcPPORNSQidBWvthYhhGhnFcRAeN1VhvyMWTjCbue0GJKiVCci1RkvWN+PC0XYuxlt2hZ5pGIeEjQY6qlinFaopVBLLsccaLynjjMPvPF2p8RwQozmo2XUPOif12D0pkqkO3qVGlDDTRhTBop3HWYLWhNR7rFGcbTddlyVx1JldjKWs0OWTCENjfKjCZmBUxCIlOa41xVkzGjESQ5MomXyCL8trgrIkswjcQmeNpivjsF52SrBPTFEQvYxI55+OhMNpjTUNUmlzmzhyWmv6JOEvVeFGg7NJ6qSoB0DciA59yS8lZNCAaT9MajFMok/GdxniN97JBtgacNxz6XoJnrWvQkRaIvWkjING+s5bQpMqZKNJ1E6OQdFE4J8ZnguZIpmk0rwUoFFU7sgQbN36DX11i2nNiStzte17e7vjw2QuuHhqa1QWULFD/glwZ6Wg4DVjme6UtaEcxHkxCW4cytZRgNRYr9uhlfjslm2P9p7ZWiIdGSnwznGJrdmPCQUSblK11sIBdGZTOWLWq2aZC+tyPR0T4IZ4aDylASVYrAmUS8GpEr6Rk6fopSuHnjlgkOxIqiSKh6LOM06hEQXYExiykz1Kjaq0NBRi1oCpT9ciUjbaQdZbOHyVdT1lJsDNnzaJ3kimpcmK0aKaMtX6tKqfCRPsKOVh0eyi1y6KikqUiP9IWXztKsiLpo6Jr1ErIua6G5UU2JjN3rGjhbS0dOhy7nIVMK+RetGAxqpY3ohj4LOqxaEFacpH5qRSYKrKnq9GdBOx1fFTCsJrtAwzYT7vZVfLGCQtB7iupOjFnTGNwjaU7a2WNswaU6IO4mEgxYVQkTojBYElMZJROojBdMkPO7FMhWiV+LM5hnUMrWatU1uSSGHOhz4Wt0UxaMVQ0sWhNrw2jNgQUkbLwy2QOz5Pn9NJEhM8a4UQta1ZJaCLeZLIRTkXGkJNGq4bOyaZOPnJGJKCUOWGMofEO55y0/jpPKdC2nkzh7jAj2boaydpFdHNBYJzHNSua7gLnVtU7CRZU7NM9vTceekbezHHMFDWjltUcU1sw4rRdtGWKQTrackJrI1pV3i3mpiUnYpQzE6PFqg9TSt0LK6tRHTknuii8sQgQqbBRkfKsHWaZwoiKkyiQh0iIeeHJlKzJKPpe+D3eR4y2whVSguSVex2on/74zAHKv/23/5a/83f+Dj/3cz/HkydP+Bf/4l/wh//wH15+Xkrhr/21v8Y//sf/mJubG37v7/29/MN/+A/5/u///uU1L1++5M/8mT/Dv/pX/wqtNX/sj/0x/v7f//tsNpvPdRHOGc42Lf2wY9cP5CxqedODgDOCPmilUKZgjUR9zlmsNbS2ISnD2aajW0WRLtYy4UVmW4i000GxvU5op1BeEWNhClnEnJwFqyvMXEOCus/MCN+iJslR2n6m/2lyVUvU5OTAGZwxAtlOgRyhBFWNso6HNZ5sGhb3YljeV1XyqdRcIOeIuH+yqIummMhEmtbgG4MxHYXad+8svrEYq9C20HhZ6CwNqoApGecN+76X61SaYRgIkzS7aq1oJwk2cik455lq8AHQD/0SoCilMUbKPcMwnehUcFTonccXilKkFJZzxjRnNOuI7S4Yw8jN/hZ/s+XJ048wruPxW6Cqiq10aRzh9pNGcsDI5yhXF4MGTEZZL90ixmKQyRZ1rpuknJdsPrU92EpnhLXVuaZuDkZJx5YKhxpbFAlC0ojxDcaAV2egTDXEu695I8GDdMeAZFMz3uArCyhXXZAhCWw8RemiilnIcbP2h6gcV2XYJKWZschnSGlHft9WdKAoVUsn9RmkPONO4mWkaoCnlMDsWpNtbc0ss/x3RauKqjLiij5M1MGJQmFNql1A85wBZXRFAiyl2lCUqjHk3LFekgCqEVxCkWYiYGMkQIqVtGukO0EpQ4p5kdef+Vs5y9/KUNuojSAnRoKqqKAouaJcN8QZ5UmzgFZVBp6Dk3utzCcIrVyjwan7aNknHq+Ut6ibTdEZTME2Gr/yrM47Wu9xzjKTqEMQlNPmnqBgjBFdktw9nUkqM2Yx/NulTCyKjW1xzpOaBq3ra4sh50hf4JALW60YtWbQoiicteagDb22TNRSYSW9H5m+3Kt+KMUitDnrMqWcoAh925tEMYltHFDFCDeCNa03pFhIJ0GklLVlYkqAIuiJdR7vZQ3o2oEk0TKltivrGijkEqWsRw2cfItr1jTrS6zv6lwuJ4HWm0KUT7ch6yoTr+u5mzrOQEwtc05kbYTHZxxZG6Y0yLNMCW8MbdfgG1+F6kTlNdUAQhsr18RM/pYghdr5Rl27jQbfWAlOrCZnTSlSXTBGM0yDlORTkeAoZeFjGSMlqAwhTihd8MFgraMtohBVMG+0sfg0x2cOUPb7PT/0Qz/En/pTf4o/+kf/6Gs//9t/+2/zkz/5k/yTf/JP+PKXv8xf/at/lT/wB/4Av/ALv0DbCvHvj//xP86TJ0/4N//m3xBC4E/+yT/Jj//4j/PP//k//1wX8fh8xTtXX6Dve8ZxYLfrKRm+0KzxzqA3q5lmAEoY4haPCoa7JyOHQ+Yrv3bg6df3hMmT8kQkobzGtAJZiwiTrWiBZB99P9HfvKRMe/p+WAz3ZO7VGajmoGTWI1CoIu2kJSVpg175hX8SQqqIgrS/He56yRBmufaTIwQI01zrq5mlKpQSa91d+DVZSWeOnI9MWucakhafIucatDKcn12QcmIY9rV2btDWYXxTN3OFNx6rNWvnsUZzc7dn34/c3O3E+rxmeSjoxySLu9YYM2JtL617RjNNUnqR2m9ZNC5inD07Mm1bTaZO5nrOWYK2It1VyniadoNvO4a+4XAbudsNfPjsmoePv0DbtgJzl0SaZFI/f3HL/jDw4fNblJEF4u23HnB1dc6mVVjjKbYjx8KYHX203I0QMSRlpXNF1xBnXp+0RluLso5sPMV5qUi4DbiMaZJkESngXKAxiX1/zbi/lY1EBeg1RVkSlhz29591UUxF02hqa/Nc+c1zvolWBltEzr7kwqCEIBpTwhZZxEVxVXRL0FCMFd5IqY7XOROyIuTMlBOqVESL0811JgIf2QRzB5iUnUrtKJq9VypCAsyRdGHuxqGSKOv7l/u7lqroTUE+UKMrYiWWBUJONcSUKWM8StF7K0GSt1UyQ2rjs3aKKjXQMMcNphgzV06EW2Rmk0m9lHlKDUqUcSLIdkp+r9yv2RxQ12TIOleRzbKsC3OZyigpT3/a49RUs1BkY3Hw6LzFhob+rqcpE2rYUYJ08sy8mhyiCG/te/p+5PYuyMalGtabc6xv2PQaZUb2ww6tAof9S8zo0INDV4FC0kjOid3hwGEITIeDaKHEiGtbus5hW4/2ngnLWCyhGKrqxoKinh5KafFFc4aU1YmyaRKPGyP38exsjTWWzXpF23aySWpQJWGtI2dFTAM5K9HiaFvOz9c0rZQomtajrMb1BjNVJFYrjHVLa61i7jTTGOvoukvhn7RnWNcwp4Gf/Xj9d6yxeO8XjmLTtKK3khQpiodcKZZSAsMwz1OPMpmmk4AixBETIFqF0c2iYaK0wtmWFEFpK+J5KRFCYRpFPbbERJmGmrQLQdk1lhRF7h6t6lxOdW+YiLEwhoxrVnjrKx+mkIvwXIouxJJJTDQFijKvySZ82uMzByg/+qM/yo/+6I++8WelFP7e3/t7/JW/8lf4Q3/oDwHwT//pP+Xtt9/mX/7Lf8mP/diP8Yu/+Iv89E//NP/+3/97fvfv/t0A/IN/8A/4g3/wD/J3/+7f5d133/3MF3HWNrz16IL+0DCMI9dKPHMubIN3lnZVORkqkUvtaw+OFBV313u2dxPPv3nL7YtATlr8KaQagvYVtlRQtGRNClH4C1Ni2u2Z9iMxxmXxmQWR7mcI6rjQ16yvIMSsphFpbaWUlA9ItcwDsppLhv9qpJ6T8FC0kpJFQRRYcy1nCAGwlh+y/FwViXxtlVfX2tA2HdaKR0Ss0tKlBjNKSwQ+7xvWGrxxdKs1JUf6Yc8wCurRdtU9eha4IgoBuWYIWmuBWquBYy5ZOjAqPJ6TiNPNtgPOzQJzJwhKzVRzEZsCUztwjJEFJqbMMEV2u54Qovx+KaQYCEH0ZV48f8bN7Y5f/fpTtNE470EVrLM0rhUGv/YUHYnFMmXDEBVZa7I2C0FSSullCVCU9pT6J5tGFjnboWxA24miAjlOaDJeZ/ZhJI4JFXboaEiDo2hH1k0VmjseicrvUHPZRaERO7P57lSwGkv11ZHaBylnacHVlfhX1WQFOBKBqFyO5ntiwKhEW6O+pzr5W8/dWnMCpkBXb5lZQDmVWXeHWbcLdBZBKl05Hjkd3wOFzvqVjUtIpnMJ5tiebJZyySyDj0qYmGswr2VjNpps7WLqJwrRecne5xLd3IFRFluDKmG/8In0MqYLlVNiRO9oFj6R+ynEX10RtZkgO3OSlgHMSbIyrwmfYv1+zVgSSZaMEd+s0LXkXuM0YtNQEDLrXLIIkRITMWTGKbPrM9oZjLVYv6JddXSrkVSgbQfBZ9JIIaFKrNeFBCgpEoaBOEUhFVWlaPF58aLM6hxJWaKS4CSjKzpwXP9On7Vk+1bQoJiWc89zCdBYutZiraVtW6yrimpqbhAwaC0t4qUonHV47+jaBucsShuMs+JpU0U5l7kzP6caaDInANri/QrvV1Ky0G45388XpNw/Zr2VGQm0RuwhyJqsUh3Pcj0hhCq9IMiF0TL7c041kBP12LmjckZnrK0ckVKItQMzhEQJUdqPUxSiv66on60eWWlOfWYdoCxty0k8i7RDEqoqpFgro5hM/Z1Yk9z83y5A+aTjK1/5Ck+fPuWHf/iHl+9dXFzwe37P7+FnfuZn+LEf+zF+5md+hsvLyyU4AfjhH/5htNb8u3/37/gjf+SPvPa+4zgyjuPy9d3d3b2fb7Thu9yKqbME27LLHTFmIX/FTL4Z6DYtDx4/JMRCTIVgOoZB8bUPbvjo6S1f+4UP2O0UZmxE5MdOrNcN63PL5kqEcIqKAnP1gRQSDsM4QeojKmvZLBTHLFAdkRP5UhajOWMqqtB2DW+/fUlz5mjWlo+eP+futhep9gg5CxyttZf+8pOja1d4a9HWEVNkt98RYyGFKP4upqrYFnDGUKqQWOMaNqszGTzK4n2LUorr6xeMY6A/TKLY2npK0qSJBRWhthxa0xBSoe8nZiOqGDLOBSFvUXDeSyCka6aiRPWwFJaATjtdSbPS1gYn6HXhNelzrTWubWQTyopw6AnjwDjsmMY9FLDWsTq7IGV4+fw54/hNphDYHUaGMfDk+TV3+5H3n94xhsAwjXzt/fd56/FD/q//5x/gncdXXG0alNcE1zOZzKC2jDEzRUGltAKnRG+G2pGSRC0EaGjcWvgOm/ew5pJonpOnAaW2ZGsYkyjmKgIl7klD4aNnH1GMp7t4izBs7113URIgxdqZkY2SbFYLHB4LBC2dDmiNNbBB2mNTgKDhgCIWhc1gkwS+mUyk0JdMQMo4pSqNxqoFsgQGun6m4rhxUJ9TNS5cIhall8Ai1ucao0DLC7JYpZRLEVNGa9L9MqaiEhbl93NFbpS2ktnVbo+ipGzUNGCLOL9mXYiIiWLUgvwVikjVZynttI3FOjFwlOQgMiso55xruUk2VG2sCOnNBEoEZTG28kmKBMuzdL1SCm0rt6AiTUcirswBXc97HuqfHkepr1SGZC4IPpIe/J/Avk1ztsMbw6oRhHMp5eZMiYk8BuKzaw4vr/no+ddpnKe1jkcXX2B1ecYD03EeA+/9D7qWoaR0GHP1GiqFMo2UlAjTxDAlrreDCP2lxFtXa67OV7z3pS9yeXXJ+tEXaC8fYVZnKN8dW07LfchfKQkGrPNYZYVMPQXhERkFxmK15tHFg6rTIfy/fuiZk7gpZqYgOlfGGB6crbm6OOPy4nwZQ7pYtAFnHdbYOhaEh5TLgm9BDUSNbWnOHtGsr0RwzVo+VTT5puMNvxZjYpymBYGcQiAnSJM8tzQF0RyJUdA2Y7CuqYGNmOMqAtbKrpLCRE4RnS3GWrzfiOKvskxDYjpEUtyz3wbOOoU3ijMvyenLF9di69J4Ucedx7dCxCBVIeXIFAr9BNqDwxLrWuC8Q+lMyCPkTD+NjCkzpsKk4usX/ymOb2uA8vTpUwDefvvte99/++23l589ffqUt9566/5JWMuDBw+W17x6/M2/+Tf563/9r3/s5+qscFGjssWiUbZawE+ypGkULYYz1zKUxBgTZEvMUKZCHjN5jOikaQ1V/8RyvjacrQ2r1qC9Ips6QaswTcGgcyHHIiV45n1cLcHJfCwk2VrymYMYraW92HtH0xwVFHMRXwxBTmYP++MSplA456vbqp3fekEYipElT1d2XrEV6kUtypDWNljrcLZZ3leyzDnDRQSCKqFKylM1262ISKnwXklVhlwJkU3Ks4L65FRmft8C/6dcO3YsQF2450yvFCiCpiytnMcLXzJP0IwpEqaJGEZSnOTntTw1TYGbmxsO+x3DMLDrJ8YpcnO7Z9cH9ocDwzSxHwbcc0dKkWfP3sIZxbp5QCmKpDzZtODW5BKIKoi4mYIsYiQkREpdOj0MpTgMnqIcur3AKkeOk5BvU6Io6QApc8dVSaQ0MRzuwDQ0m0vKKxyUOVOcO2UC1ZKh8iJiERn7XEtiSglRPFHISc43VtAkI6iEKiLnXZkFiwme7H/yeZR5wa7jA3Usa5Vjc6SQlk9+9R75VLJN2ZNmKSwESckSoMxZ4qsgwSxkeG8Hr8//fgyr6iIvfIK0IJmStRelSSrXTL5SS7WuEud2IbpqJZuhCMJVYnkBZczSwi/IQrU7MGLypsqxbXoR2ZoFr+rgn7kqshnO9/Pz5uH1ZhiPsivc+gEuW7ArvDGVkyAk/1jVh4mJPE6YA7CPTKrBKEfSHvwK3a5pNhO+JFbrrgYoorUTE4t4ogoTpEQMkX4MFLdlipFxmlidrWg3La5bYdsO264wTSdcLmM/8VpVDUiVqXwQPdtH1ABGadpGNudSKkJ0wmmR9tpZkFAc19tWeDhzUDoP3oUXVMfX/FyOkIqsIcpY4Z24Bl1L2JJ0cm89/vRHeeWr+YPnuZKrxL04ducUKFnK3rOC6yLsZnQtR1VBtzKj0OLMDYqUoiCVSpEzTGOmt7Juddbj1LyOF8ZxRGcxEczmaPSn50oocqMKZSGUL38XhFCuhGM5G4TGVFAxkkxZGiY/y/H/F108f/kv/2X+3J/7c8vXd3d3fOlLX1q+3m8z73814JzBWIc1LToXnj/9BtpkvvClc1btisa37LZbbq93HPY94wibRvPOgxU/8L2PGYdMf4CHD8549MizeeDxK0NUgUxiUkr8Loxnt8vchkQfe8ohwAQqgCkzOqJqJ89xZV2k2+UL8V3Iif1hi24KrpNgROGZxgJZo5U7ToRXAp4HF+colehHGXA5ZuIYGfoJq4W5vlltsMZW5Cix7cdlw2m8Y9WuAE3KGW8hWpncJWeG/aH6lRi891hnmd2Jnbdo5dmsVsQYCNMkHVJo4hgX2WjnHFrVBbOzS5kgRTH2mjsxShFyV07SaZBiZCTJhpDtcg+ldTpglEUD++0tty8/5LB9wdTfChcHOPQjX/3a13jy/tfIMZBTpBhLUYagGoYAISaUtqzX5/Rj4IMPn/Fv/9//G1fna374//57WK3PGNUaVg2btzfovkf3PRbpCpq2L4lxZAqjkMtah3YrjLtksmcY17B6/ABSYOweEMYDh+1z4nRg6LfodUdDJmlDjpHdYYtrEl3rUJPlcDIH5k1vKDCVwpAiRovHyAxc5Jqpi+KkNCRJOUJcRjOiEFuoJZlaVslKkbV0D+g6Zu95uNTMUhUq+qfAGLQVdEMW1sSsCkwl65Y8ByD1Il7dnebSyxzJLPNFDq001jci8FdLSHAMTiiQimLINdA31UK+kgIB2rZZ0JFCJmlBLou2JG2rGq106kRd0U5lRGvHVyJrgZl3YypUXoqUL7U2C4KS4tzSP2tCIPyXGsyBrvoc0vUAVc/i8+xz9fobv2KzVnzXl34b+/2W3d21GMs56eCZSZcFRSxZiOzaM6FoP/gm1ltoLLnryKszus05xlrOHzzA+kbsH7SUUK2xGKXpKkE7jBMvX97yH/7zL3C7vWO8uWbvICrFhVqBXvP2+gF2dYXxK7BekKdaZjyFjCTQlGZyo73wQbSRIA/q5iZt1cZaGr8S9emUSSGISWAIjGMQjqH3PHhwzqpyT2ZnyRILuUjg1njLumvx1hJjwTsZlEpXdy3nsc2K9uwhzfoSYx2nJPvP/rheS7dEhVa1yxo49TtynpgGMQvMSdUAH0lwFEw1EfMq4Sw0Tsb3MExSbimFJnUYG4nTc2IsNLYwkhjGiZgmDnvFyl9gtCdk2Yf6YaTVhk3T1GBNgpZSEs1Kxr3SplprCAI8hpEQpKymrHBiJEkQ6YxSFFMsZPV/gADlnXfeAeDDDz/kC1/4wvL9Dz/8kN/xO37H8pqPPvro3u/FGHn58uXy+68eTdPQNM0bfwYwJcvd0GFj7fG2DSUXDnmDVoW7YU00DemF4/bWc7fvGAZNmEDZS1y34uqBJ4TMOBQuLhznlw1NZ7BeYUokl4xWQbIyU8SLvstMlwOewEUHMai62kj2NkO6FTapMt4KXerCXgLNynD+wLE66+jWLemB57wbiZOgoCo7GdNJcXFxfu+6M2aRujIm07YblPZo4+m6jqbxOL/CGoMyBZMyEYv3TuSXq+IfRbJb6zxNLmzWUk+UtVYibJHFNzjrq2CP+PoY65m1ZLUV5UzrW1TKS3AjHSaKFGWz0wpSUuSsq503R8Qmy4TMxRDTjESdXHPoGW4/QCMZbehfUMKWVWMxuUMj9elcJ0aKSTpNTnD0rAulKLyVrg+0XvQ6whjY7w988OQZXXdgVC39lNnvAuM4ih8J0u4R+p6SAjlMKJMx2aCTwsSMHga0dQSjICfCfkucBqZpIk3Spjd73JSpkoWR1uSp3xOn4XjRSnH18JGQBZHr0GRmHqeiBgQ125w9Yko6ga4LS5BS31KCRSuBSVZqQRYy1XcmlgXxEsSjcl/07MB85IykmtHmU+h+znCP8cf9o6auc1BitLk3z502PG7OSLW0sDBh9NwFd3yjWcnVGC3KtPX73kiAn0jSpeITcyDknKmvl8wv1bDBanO8zjpmZjVUXYOj1hhmYcQ5gVg8guasv2boFlvN/MqC7MzIkEXTVu7LfDx//pxf//VfX2D/VxVJT49QbTpub24Zhp7DYcIoGG2VNJ85NErQsmmaSFEURs8vH9b10qJ1S0qSIOVsmMYqlFgyRkcJwI2YKZZ6/9OUCLHgfEvbJs7WGWcKVkMujikobm8OxGCgPKH1LS/bl7VKljnsjyF4mALvf/MJTdNgrGMcD0z9nhe3I7tBM4VGVpkDOFPwTrL4lIqsJUmxHQ1ThD5rSnK82MMuRO6maUFoxyCJ0WHQ9KMj6jVKGXbBE7RjKGYp9eiocTmjP7qh3Rdue3USJM+77ScHKqcl/pwTz549u9fRElXLaC4WxD14TdKBnAZZg7M6InRaODdq7kO2mWwgOlHdTk64IKVAbBrhYWlH0gW1ztgy0OZNLd9AXm+IraNvJCEN6xWqaend2YJmRyUl/FgD86kZidX3K/oVyq2ISp7FVLVYQEqlppb2c4Gk/Cfep487VHkT8+rT/rJS99qMSym8++67/IW/8Bf483/+zwOCdrz11lv81E/91EKS/cEf/EF+9md/lt/1u34XAP/6X/9rfuRHfoT333//U5Fk7+7uuLi44C/9pb+0LGgL3Ukt/zsiDmr+doVa5zdaFs5yfwFV99/mTcf8+rJkft/ytD/2OC3/LJnk8afLB86eJh93RuX0whbU5v5JHxHMN73Pt74O9cYbc/pLx3t8fOn91yte36s+/XFcJOoZM/MY7p/nKxe+fPKbz5pXXrF0pZy+9t79/eRzfPMtKif/PEXD6kvm4bogBMfX5Px69vWbdbz2KZ8vYfzsH1hh/NNnlz/rxFJv/vKNj07d/+ebR8m343jTYHh9TszHUtr4DMei3nxyfa++RTn5n5Q0TjuQ6lksS9HJmvTKcW9mlRlhK/fu7T1l5fq+6pU3O9U4UlC9vk6e2Ol7LyHnx9+XV9e/ZVlV6uT5H0/y1TmGUvfGy/JJ1Uvp2zEyjknr8SzUKz//zTzeND5eu+ZPGq73HzNzGXh+o4/fnY5vPI4jf+tv/S1ub285Pz//mN+Q4zMjKLvdjl/91V9dvv7KV77Cz//8z/PgwQO+67u+iz/7Z/8sf+Nv/A2+//u/f2kzfvfdd5cg5gd+4Af4kR/5Ef70n/7T/KN/9I8IIfATP/ET/NiP/djn6uCZj+Ux3797y/eOk/NNM059y4DktV+p//tcZchv+caf7xeP5/Ixb/IJA+j19/hsn33/y09aRn6j22x54yT+5AX9zT/7pN/4vH37crw6i7/Fq8urX7/+y68GTP+9HPrbNMG+1dT4Fj/6Nh+f/Em/sbF3PL51svGGMVXmv8q9r9/wkvvv9TFB+THA+OSjwCtClK9/wsd99se+dn7xJ96Ik5N+Zdoeg5pvz/P4uM9/bcP/zfy0N7z9G6/53i99zL/f8L1vd3j1mQOUn/3Zn+X3//7fv3w9c0P+xJ/4E/zUT/0Uf/Ev/kX2+z0//uM/zs3NDb/v9/0+fvqnf3rRQAH4Z//sn/ETP/ET/K//6/+6CLX95E/+5Lfhcn7r+K3jt47fOn7r+K3jt47vhOM3VOL5/9XxaolHEVFqXFr8ck7kFLm5uUZRWG1WtE3L5myDNQptRFk1pcRu20vdLlupKRtbo37RmPjYiLZClkforyxwJ5xmwUdY//hOb3rtTDacO2gqKLY4JCu0adFmtfzO9u4jKJG3r85Zdy2PH1xUYmNh1tZWlX3u242w0rVd6oM5iZFeyhGK6ICM48jXv/4VDoc9d3e3UqO3mqay4bv1Bus8rj1D2wbbnGFch/WrpRW6VEp8SsLdSUk0PZSCFAdSmkhhRykJY1pEQc+QcyClgRj2xHigxECOmbtrTapNLVM5sC/PsNVKPuwjcUrEobL3nWihHA4jMUViCqzWDt8aURbVGmeFBBanUI0Zk1i2Z8XhIPopcRI9gtBYuGjgvQ3TmWLaaLm/FBgnVCq4BF02PAgNvs80h8S460mj1O6NM6zf2pALHA6BMCSmvZxbzpGzi04cXEsiFXEjvmwvee/83XrfFO99+X9hff6wsvmrW3YlLM/8mtlFdHeQts8xpGWsLmWwqlNSsnRghShtllMS88CUhHmfKwM/lePXOSeW0agk95tbZlMlh+clez2O8VP4/+OyaSHmZr7YPqXTwr+ZpokXL14wd3a9ipJ9qzLIJy1tp7/7xm67T/V7n/jS+VUf+/vz0TQNV1dXy9c/9EP/C1/84hdfs3mYSxIqZ1JF1lM1tdPhAGNPevkhJQRx0G5X0HTY8wfS4usa5m4wxaysNMsefCuU4duX2c/34Od+7uf48MMPAXDO8ejhW1xeXPDel764lLPnMtSMIMYYub6+5r/+0i9hrcU3Dd/1pS/x6OHD5X0/a3ns23F8/Fg4Lbspbq6v+dmf+/ekJHPz8OHX6F9+gNEaqzVXF1d46+i8YQgT1/stj7/wRR6/8x4XDx7TdCtSnVepkseFLByJtR1ZnLdl7KQ8/12WuTirYOfaSTl3VOa6bs+ddWX590mnHfNakO9d+JHqMOtxyWtnZK34DaV2i/6mlnj+D3mohFajGOUpTUgTJU3s7z5EKWibK0ybWbcrnFc4p2TzioF+tyXlRIleRMl0QylVo2Imyd37rGPAAJy4NNaHz3FRnjfq5VdfAdNKbYkrJS+vnwmB8wA41qMVVuklQKEUDvtryCPugeWsMbz3qJFe+UWpx6DbtXSTXDxGG4cyHqM1zlhSDKQYiDEgyq0Nu/2WmxdfI00TcXqB8mIF7u2Ktmm4vNzgO0t3doZpNvjNW/juknb1AGM6tPJ1E8yEMNa++R6qHH8Y74hhTxhekNOE8+coZck4UhoJ4Y5pgGmYyFMgTpndnSJFud+RkV3+iMZYGuPow8B0CIzbRMkKs/JMU+T6es8URsYwcPmwZX3mpH3cGLpWSGDDMIgx4yFSovh7XL+8ZRwmhkMkacWwachxQ3n3EYdO0z8Q3xxVCuwOqJhpA1xEz3ezZn2IrIfI4fqOsB+EhNha8uPHxAw3w0B/Fzi8GJnCgZQCj7igaR1jnog5sQ+RdJGXAAUUD9/5Mg/f+R4oqbYQi1CTtWaxA0jFkIri2c2WcYrsh0kCYy0LfUmiZitKlZacYZgiMRbiJC62KYq7dYiZQSViEi2RVGogi5RddJGpkHMUcl9Wi3ftPKbn88rLIvZKeFIDJ5D2VU3isX+5BCghBF6+fPnGksex1fxkRn3MLvHq9z/VBrbM8zf93glHJn+r4ObIn/i415ydnXF5ebn87Mtf/jI/9EM/REpp4V/I/xUqJ1RK4jcExCq+Zw7XlN0N4asTeezJvYKzC9hc0HzhezDrC+jOhFWNFh0fhKRt0Cfn+NqCV//8xkqMc5fK6T34tV/7tSVAMcby4PIh7773Lr/jd/zQ0gY8d4c5J1vVOE58/Rvf4Ctf+xreN2w2G77vf/g+vvzlLx+9md4wNn4zjzkY/7jvn/7s/fff5z/8x59bApTh5kPuvv6LOGPw1vIwfwnXNKy7hjzsidcfsXp8xhfOv4d333vE2eUDQpaAI1TC7zRNhDAxjQNxFN2UkkUIc9b9ibXDLJdCqYFNTtK+HGMl0deuu0yqmjepBhulvlYClVSKjM1yUm6f97FS6u/VlvRZsdp64OMbXT7u+I4IUDTikTJ7YNze9PR9zxTBeU+3eoRxDUOwRDKRTNNY2kazuWg47AY+eP85xnhWq3PpXKmOmwKQnBbZytI3fprZHYmO5d4f+ab8lcuCk5yQ2rKoeFY1wNMgB2bfmdoxcVILLYjoWZwCH3z0kn6YWK9XnK1aLs9WMilypqRMInLY7tDW4ds1scBYCm5GIZIM1rFMqKz57i9+D51vuLt9SSFQ0ohSTtQLncJYi7YN2nUYv0a7DlxLKrp2uESp26oIBMgDOUdynkihp8QBUoAcJchSwlAvJCgjlCiqkMahbEWE6pFjZjwEtAebNHGYiMOEUx5lDdZ4lNF4O4pNeOPoOo/3lkN/IOeENgntHO7RJWF7YAi3dM7R4TBxRXCWu3hgygWdC9MwMbzY4dYb9GVL9ImsMmhNMYW9SZQccT7wFpqutJxjMZvMmCdwYDqPorDO4rDqUaRRk2Og1Q6VNH2AoC3hfE1qu1dGudyD2VOnsdJO2zhDiJEYIiqP6FL47kcN2nRYO/vvKKiGfbvdyDBGtrtAiJkQxHnael/JgIYXL+64vdvz5MWe/RB4FlINMkT51xpD6y2ts1glTtF3+4kpFvqqPpuLIFBZVWn9coIOnlDmZr8qXcHKT7utvIp6vBqEfBJ68nE/e9Omdop8Huf0nN0f3+uTgpNvBVK//vMaFBhTkbHTH2npYIsBkyIqH8hxYHryq4Trp2z/9/+NMhyg35FWZ+TVmrPv+0Gah2+z/u7/Ed2eUfwVs2aLRh3NuF8Jpu6fz2+cA/VxqBVAyZl+PDCMAyEEiDIe9vs9MUYeP36M1orrmxtevHzJ8+fPcc6x3W65+d4v0/c96/VaVIX/Gx/zWJDrundVwiFdgtjXyc/bvvD0OmN0wpjAy/GDKg/REMPE/rCluXhGu/kGq805RkPR4q80hUSMiWkcCDEQp5FcRfly9ZmKVX02VTG1WStFNFbSokk1JxIlJZFlyEn0V3KSoCUfURGRD0hHXme9pFz1WnKKy/spM+sTfb5CzXdEgKLqIqhroBBCYJwmRIHVobQnF8MwZRpVUAYaJWZivnGEKVJKIMZCCKPIvmsnk6h2/nyrbO3VNeZYvpmf33ERndGSUtJJgBIpM2qjqufHyQh4lSU/v3fKhcMw4d3Idn/AGU1ed6DECCunBEWR1YjORTZ92Wsw1Rp3rlikIG7DZ5tz+sOeVddV/RQxgZI/0uZJFa0SZUhpJZZzFIMvEVoLlBzIaarlm1Gks/NUB36s6qKIj40SqzexTpldYw2KxFIqy4UUMlEloo7VuCrhVBG3amNI1RRy1u21zosJWRJYNKaCdWAbB5MFL26etmjaxuEKRB+wWTRKyIqpD9ghoUbAKJIRKfRMIarIWAo7nTjzislD2zh8bYMstm7EgHWa4g2ls2SVKUHIirkgkuDWkLoVubnflqeUbOJaK3EfVap6bqgqFJcpSe77qulovMiCG3OqBgm3TtEPAaPE7DJGERvrVt1ilmbLiNeBfhhQJO76o7Oyrq3G3hnaxuK1RpXMNCW0SsQKJ59Ol8x9uJ6TAEWG931U8tXj4za2OTn4NMHJm173Sd+vb3Rytvd+8CnKO8f5P79WlpLX15KPP44R27IKJNEzmvqeOI2UsCeHA4dnT5hePmX//AlqPGDGA2nYkQ8d04MLlE6sHr9Xo8A1Solrt8Qdr7Igyytff8IZfgak4pMCtVw1WoZRWvHnLqD9fs80TVxdXWGtZZompnFkrJtySom+7xnGkdVq9anP5b/lca/c/8otCLEwjBltClpDYMDYwJCEphDHyO7Qs73bMRwOhLFH24aMIsVMipEYJkHDa8l+QUiyBAvik8Oi/lzSbCOQj+UcJEjMJZ8EJscApeR8Qhou9wc281a11I9rqSijqpjnp6yHvnZ8ZwQocz5QSyuH4cBhGHj46G2McdxuDyQysUSuLs+4vFyjjcY7xdnmAV0zEqrJ4IvnH9F156zMVa2DHks49/wzPqYePtfijv+GWQlWG2kVldphYhgPMghTwHmDdYbziw2+aSh1c9veHaqC4xsWSl3tt4tmOwTef/KMME103uCrW/MUJjIabI+2nhBHnHU0vhEDwaIxxmG04/ZmB8Dm7Bz9VkGp/5Hn19/k+ctv0q4crjFkJfeRMFL0iA+BovrFfl6cUyMlR6Z+R4oj03AngYeKlDRRUiBMe1IKpJwRo7AWpTQGV5VuO8bphhwGxH1TjpwhDIVpGLhVe9E1IGOBRjecrRq0d6xTYoqBMU4434grpz6QYqAfA8YovC3ojefcPMBuA+oQObtaYYrh8vKKlAq7kNmqwEe7kcMHe4Z+ovnSGVx4Rt+JhHMW6/kXakABOSm+MFguDhplBU4/3NyBqVL9TuPPPcFBHDW7lJmUJj96RF41xLcvyGMLs9q9gs5pzlpz1MaoAmghQz8l+n5iCgK7bs7qGhEjs6G19RbnDFcXHednLZvNihAz0yQCaI138kFKcdY8ZHx8waMHV2wPgV/7YMv2EPjodk/K8plt6+m6hpUVlUnrGsaQMNuBGDNjTCI9HkViuSBtpBILz+jkSemnVK2VV/Y7rfVrXIzPcpzO06MK6OzhYxfjytdQz0/xfm9KSu6XgT7fOc9H5gS1KRmVI9ubl1x/+ISnv/4Vbp48xQ3CPbn94JcpYY/N12wMPHQGx0gbM+PXfoXp5RNMTNj1Fe78y5juDHX2GN2toO3qja+eY6ijXs7JH7n+39AlfewxjCO/+Mv/lcPQ88X3vriIzH31q1/lbnvH2dk5m82mKvYqyIVhOLC9vePJkyecn1+w6jrx3FHzs5k5KZ//vD55TLw+FuY24qNLdqE/DOScaZp2UdmeD10KJme8cRhlsMWjsiZMmqQswa/YjpEXL1+yu37JWetwqw0oTYiZGCNhGElJErWy+JmFWtqJVS9GTnJG1mU8ZXSZOShQxNxNFo+cJJAp4suWK0o0M5Y08/iU3xWV22r1ghFUpgZKJn8cMvetj++IAAVmMqk8HLJUw9u2RWnL3a5nihP9eBD11FVLY+WGeiuSxqt1K86+FpTKpBjEBVfNtuhHeO60J3657adN5eVk4HKszaUKnYXQC0k3iwJp2zqarsW3DavNGb5piEGkpJUemOv0ryYzs4W7rupdU5gYp4lhGJmddlOZ5adm0acIyhyt3+WblBmqq5G2tZbz83P68Yb9oa2eIuqV2rwgJhKQhGoQRUVOIjEeiHEkhANGgzJH8tVpFj1D+5rZqGtGS17XwVAo0LYWyiC7IiJwSpMMRLJI8xuF1RblFE3r8d6LrLlKFCWowxSCGJw1DjUk8f1Q4kFiPagELZlYCqsSCYdInyfMuQetMVdG3IGVqZ42mUnDYBSTSgQKJkqmEfsBrEI7gy4GoyzFKVBWFl1j4KyjrDxTZ4n5PqRujcbZ4xgs1THXGCr5974+zOngnNV7FeKei5bfK0URKIKwzT5ISmGUxjtL13pSVnSNE2E5dVRHTrkQYyYoUa5VWswtrTFiGpaqeeD8zNSJinLd/lQde8uYKq+cP/dLKK+iD/c2j5Ppdz95OKkb1ddaKw6yzkkwnFIWv5py/7wWWX+o/i+gdLVvqLD3p2mN/dyBynxpJVNiYNrfsfvoCR/9+q/w4mtf5/ajZzQpQRh5+s0PKamndRNTY3Bdw8orSlLk3QGVM4ePnmC7PXabsetL0hRoLh6g9BXaunptsxaJfuWsKyp2gnh9W48inIUQIodDj3MWYzW3d7fc3Fyz3W7rs5LSREyREAUp3263XF9fM44DXdfWMs8xQCzlsyE9n/6c7/21EFZDCEzTtHz2brsj58z5uRj+nY4HEWETorTzfkEcphBFEdorYkqM48BU/6j6rARBSRU9mcs2eSmz5FOUJMmZLmhG/TMHv3XhpuRESomhH5jGUdZLyoKYe+s5HdeSWMwbx/17c/9n/x0HKFprjDWEKRLChCXSmsLV5YaQFd989pK7u1ueP/+QkguN8xhWIqGeM9ZkHjy6om0bSkz0h0S/v8GaS4zVYGz1TalL7MJgBvHIgaLn2udsMFa9ZIqQDmPM3L58ydj3HHYvMKbw4NGKqweXvPel91idP6A9u0JZT1Ga/e0tQ9+jb3syCUXmVLdAKfBWo4qhaRRWZ0IeOPSWl9eW8/Mz1oXKawFlE7pJGHuG9RrXNZii0VkmTcoZ3zhyLhz6Hmvh4YOH5HSgpJ5tOBBSRGMw2uKswRolEXbR5KIWF+KSR3IKjP0NKYyM/U42g6YTUmFRQtZVprorK4x29fqMlE1KIuRATNO9sa2Mw3bnFJspNosfDtJdkoriLuwhFbLNdF3HarOmaT3Gaq6fXxNiQjtLpHB9c82m61ifncOQKVMhpELIqXZwgddwhsGojvDyGdd3L1AhYh5v6P6nt/Erz0gglMCYBiarOJwpDrcZd8jYrXBt+mFPsaBWDm091je4rsO6jvbsAuc9/blnMHBrA4fpODUVUhryjZEymAJjTZVal4BNaY8bZ6M0fQwma6ePQkMW6k9MhTjCNMH+LtTNtjqZGiuBqwKxmzd4b7CjmJeFmBmDdGMd+hFvRTm0W60oSqG9FU5DTBLca8m6SiWwy/o4l+yqN0qefUZe30ROg5DXSz2nf9QyF4937dVDgrjN5ozLq0sa71FK8/777zOkXjxLkGSniv1Xzo1i3Tis0SjXkgsMYySEwDAMb/icVz5VfcZSCHUVKVK6sykybG948cu/wK//p//If/63/y/CIA7qm6sHJAo//19/lTBNrFvPw1XL7dUZVxvPxcrjx4z1PYcX/wmUeHy580dsvvR9XH7vb+fqe387fnMlnjmmqSVbuyApuhTMfE9/kyAUYyxX55c0tuHmxQ2usVhv+OCDb/LhR0/5ru/+Lh7uH+Kc49Af2B/uGKeJMQS+8c1vEFLk3Xe/gLWWi4uL2t32G0Ow4BQZ/9bvlVNmt9txe3fHyxcvJCFC8eL5C3LOfO/3fi+7/f7++5eCKYUH7zxifX5GPPQMfc+T919QvMX4NXEa2W9v2d7ecLZqpHRiDGMqxFyYwtEeZEYbX/2bdCxLzn/PPyuVM6KQJHp36PnwyVOeP3uOchZtNG1jabzn8dVDjBEjwtlkcS7dHlFIxMMtgtJ1bn7OYfMdEaCIzYVkhkpJtmM1qOrlYr2joOgPI3e3dzxvG5wFrTucE1+MlKUFtVs1GB1xNuJa0Fas6gtArtO0VEcSdUr4k+6KoiLoTCaQSmSKgTCMHPqJ/faGMI50raFbeb7whUdszs/ZnG+wbSeeKcqSi5xPyrNM+bwA3z/W646cNV1n0KpgS8R6S8iZKUbsNEk7bnXfNQoWE76UyCFWOXTZMKwRa/oUYo26FY1tuNhcEfcFwijdKzmJxHuayHlCJY1KjpKikGHjQI4TOQzkGCAnVBFPHm3FV0cFLbybGACZ3GLelSlJzP9C6AlhoBTPjAApZ7FnZ8Q4kNKwWI5PYSTmQp8iBoNrLbYzNCsPJRNCxDqL7zw4TVFiM57GiUEf0GNEpUweM6SCmsAUTWO9kEKdZTW1bMKK1CfKywGe96gzi1trStZMSTaWoDKj14ydwo0GlSRrz16R2obsHbFpoGnIzqNWjuQsmILRmRWRhnTvWVur8V5anLUGUxFArRS0BoNjsuIM3XqFteCcoBrGSqCQC8sCokq5t3EWlLQZl0Qugibs+onDmDgME8MUq6eL2ALE2SSTQjIKFcRYT1xtpWW5KEFWSiVkzvkUC7/qiIrMs+jT7H9HLte979b5eMpkObVJEJTJWov1jTjTGpnPrmlJucgasqCVwl9rncYZRevAKkiqkE7AzE/ksHAfbX3TNXxS4KKBkjLT7Q27D5/w/n/5eZ7/yn9l+OCbXDy4YnV1wcWX3iEpzVe++hXubu64u9lThoBJmcPYsh08m9DivWGzSWhVII8kIuaFoTuzjGsNw2NMt8GsH6Bsg3IiAZ+VrTmwqknyXIr77DvOJ5JkSxH/rXHg7u4WbRXaKK5vXnJzc82TJx/QHw4Ya3n65AN2uy0xJZHvD2JBEaapIhTyfvO/Tz9bOn2O3T7f6nj1OS2bepaV3+gZrYFxGvno2Ue8ePmCp08/pG1arHW8rK3yb7/9FuMwvHbtijofmRHmvADmGvEtm4aRoT/Q73dYa8RGoyhyqdYgKRHDtCTF87NaOnMqyVWuaS7FVxSlOmxDoeQIKZDCSBgOOCUyELqAyoowTSST0NX5+3T8zzyVnASFSTGijKIU/d83ggKI5boWAz5vFTpLqUFhaVeyGO22Bz5UzxgOB9pGFu/Vak1BMUVAW84vV5QYKDEyJUXMiWGSxVv83aUEoFVBq1yRjSxfU0CPApnpwJRHcuoZdrfcXG+5u96Rc+Hd3/ZdPHx0yQ/84Pfhmg7t10ylZSqOkA0pwxiFxJhTeaOQoVKKq6szrF2zPvOoksnTCDETYqYfJ3IpPDhb462V7g+AFMkhEMxE6CfiEGgavWTopRSmPkAsxAydW9E+8IScoNyhc6HESAw9RSlM6GUVNuLYK8SuPSmOpEk4NqoiEtZ4tG1Q2hLMgRwDKUtnTYqJogvaFFLomYYdY79lHHpKuWIOUIxvaNePONy+JN70OKvRujD0PTFFkrY0vqHdtPi1pzvz7G639Ice33lsaxkRkpgeIiEk7g4DPhps1MR9okwZNWacsayuPKax2LXnUl8QnOZ2u2e83cHKoS4bui+v0UozJrGkH1Xh0Anp9ix4fITiC6nRlMuW3HhC60lNi3Ye3XqKMdIyTuJhCWy4T5L1XtN2BqvFy8jaWvdF0TlL6jRhEtdaU1s0JaA5bvo5zxoGZVn8TO1Wy0q6wkIM9FPPFCaudxKgvLwb6MdMUVbMMsmEWkpNgMmFiGwGU5CyR6glErQR9OZEbUMxeyPNwZGEFXqBbk7m9cxPOV0Iy9LrxmmYMr/PspmerolKoY3DtR2+W+O7NXP7ZNOtBZmbhgWmV8ajXcOmNXRW0ZYeVZLchzJjHJ9v0f1Wbc/zT3VR5Jjonz7h+S//Iv/l//n/4PDkA6ZvfJ133vq/8T3f+yUe/c8/SHSOr379a3zzax/w/q98k1ujuL7Zcn7RsTnreHS+Yd02vPeww9uEzi/w0wtKekajrmniR8SH72I3l3TvfB+mO0dv3gLToM2KogyLc/Xxf9/WskkpmWEc0VvFRx8+JZdMzJEPPvgmz18845d+6RdZraQ78frmJc+fP0Mbg2s84zgwjiPDODKO0zJmxOjueK5KKZrGi3+V+nzdPkdehwQRumlQylCAw+HAr/36r/Lk6VO++rWvcXl+yXq15ubmGq0177777psRtznpq5IPKcalJKsLpCnQp8Du7oa71qJ0wTpPwlG0BdtUw9aBnCpRlRmtlAAkn5Bc80kpVeaAmM0qMsQJ0kiuJOvWKbzxNMg87w+9mClWDpf17qiVUktKKYyknIkpgTbYxvJ51Xi/IwIUWS7KCQIptmeUhNK1JbLxNG2Lbxqs91jXYF2D0l5KNHMEv5+wqmCVomt8zaJEwCpMQ0UWiuiNKI2pwYlRQYzcywgkCgGrE9oHeNBwvvbwpffQ2vHgrQc0necQMuSJMmqxu1dSwigFwjgSxqmSniqCclrqUArfNjResT5bS/IXI3mStttcEkMMjFFQJK8BFSkh0E+J29s9ty9v2N1u2Zy1tK3j6uIKZy1eO3LODDFgrcY4Tdt0ZBJ9mkhjJKWCngZKKijboe2emAIpJ+J4R84B8ljDdVURl0hRoi2TK2NcITB2iqMEKiUx7LYMhz1pCkeL3nqEGLjb3jLe3jHe7vCtxVtNOxVigbJSaAtJZYYwcLuFfr9n6gfJNihMORJJNNaKYy6mlrs0Mx9HK3BaY71FNw66hkurMesOE5+x3x24/eCOeGfIZwoahfNi+hZUYPDiJzk1QpJFS5u28i3JaCZES0SXgq8lqrVWqKwwY+Y83N/EnIXWKowWpMDMFRyEj2KVwShpf5/966yuBW4lmRZGLT2rKleqajWwG4Jonowpcxgj4xjY9hP9mKTbJ8n7zF1cCsmMjpllnYs18JDvy7XPDrZz+FBKdUnOs1vyUkD4WCj447tfFiyjBkKy+CqthUvjW4wx2KbBOIdvW5q2Zek604rV2TldJQemnJlClJ9riy0TBSEa6izolS4wF4HehATcLwscg6hTIbFPCm2W8Gc8MN3d8vQX/zPPf+2XyC8+pMkTzcNzCD3bJx9gjKIYw9l+y2UeuVgpAtCrQAmKYR8JMbJuPA+uWoxzbDaXeF/YrAo+3aBuvsIUbpmaM+K4w6wu8A++iG422PVjMCswm4XnpIytOJWmlNnneX4Maim4Mf9kDhLuPa/7xzRNfOOb32DVdSLwmBMxJ25ub9jtd3z40Yc0TUMMgUN/IIQJUyxKK8ZxpB/6auY5EaZAjJGnT58uhGhjpEPt8uJCTFTd623Tn0b47+7ujhcvXlTukeLRo4c0TYO1jt1hzwdPPuCjZ894ef1Sys4pcjjs0MZwe3tLiOH+mKllmO2LG8bdgWmaiDEJ+ig3j1QJ54f9nt2uwTkrfBW7RjlQTviT0zAs61eZ5+WMppyQdguGjGachEumq25JypkUEmmMXHYN7aNLJm0p2rBab1DGMiVDjIFpt6PxDb5pZDyI+qkgQHlG4CukXBSvNMt/6uM7IkABga/n+rEs3AVKQqtM4y2+cTRti/Meax3Weazzi0MkJREz9EMUAq3VNM7RNA3eTKQUGcoobV1ZdDqsdlgFRhdsGdAlAiNFeoYwpmBUoWs7sm7ZXLyD8ytU25BKYTvtiVMgZlBGi36Hk6U2TNMC2Ul0+/qE8k1D02m69ZmQ+CiEYWA0e4bDnjiNTCnKBC2A1tiUGIeel3cHPnzyIc+fPefB1RmrVUsaR1bdireuHlJiYjz00DYY09K6FlRmuOul+yYEdBjJCZQ+gD4QcyTlRJh2lBLxVngzWltKjuQUhGdSZgGmyhgvSdCXOJHGnmG/YzrsZGN7xYs+xsBu2hJ3O+J2jyorvLc0k+y/0Uj7eFaZIUzEMTAdesI4obRYz09pomjwjcEog8GikkaqKrreb+EfGGcw3qIbz1nX0CoILw6ofeT2w4+Id4ryhTXqzOGuLBlR+Rx9wSoITmGLQmHBWLGc1zAVMcXWpWBKxqJYa4XLinbKnMX7E9ppRWPv64WoSnAVBEvNshnMfsaqcjwydbErikomQBuFlloPuWSmGJgSjDHTT5F+DByGQD8KIifrsZDGlcnyXGZSrlJLgDKjIkqdBAvMyMlxoy6lln/KsSVZv1KgmV/78Yc6+XvGhORrrQ1GG1arNc45ms0GYy3We+kMot4UpVmtz1Ba4Yx0H/RjEBJwBsYthKNIlXBlXt9mX0V4PvFQn+xVNb9HHnum7Q0f/eovcvPVX6PcvMA3ltWDM8rUs/3oKepwAK1Y91suysRZp9jlzKEkUhzoiyaMgYNvGMo7rJyjfeBobWBle3y5g+2O0N+QTEsMPXp1QY49dn0lpYfmEtUYkRTQuvJodS0j1EBlDrpKWQKUY5BSg7S5eeANzzTEwAcffsC6W+GdrV0pkbvtHYd+z4sXz7HWMgwDKUVSFNFApRVTmBgqijKOI1MIjMPAs2fPjpm+czjn6FoJWJ178z2fT+0e760+21IK2+2WDz54QqwkcOcd6/WKtu04HPZ8+OwjXrx4wc3tDc5aKNAPB4wxbHd3r4kOzu+7v9nSKyXdLwpwM58qk1Ik1Hbqw35H23hSjNjGodHYTgk5dxyP71sdvRcRuQwzrFhUIWPY94UQC14LiWFMBRUTZoqcec+DB+c87xNjhna1BmOZ+kScIv3ujty2lBywTYc2rr5/ppRja/JvJDiB75AAZaHJaVFF9N6isTirUFbTKY/VmmGUGuV2t+PxWw+xztE0IuKljWzA6I5+PLDf7skpsGocXiecyvgmUPysqqfIZZIARYHNEwqp3RdlKKajsQ5cQ7Erim2IbJiy5XYbGMLEi9stpWhKaTg795ydGcYUoCTGfiAMUyUOHhf600Mpi1KOQkOZO3OsAQsTE4cwUm4PNHbi3YtLGmcoGGKCfpy42e559vKOu/2At5ab51s67/no/IyCaKysL87ZnJ/jGoUyDoeDkhnigZIGYhpQukGZ5lhCiAdKTkwhobXBNR0xQoiOHHsKmhT25DgxHHbkFElxQuUkUGeZ0Bac7wDH/mCYKRmuwGVMRKWI3mKmRAiZlWlRVpG8Iyro9xO2KFIWMbc0BTBJShkloqzCObmWnKOw3CcpPyhTyCqTbGIqAZc0TQisnMNYx9B69KrhbmuxU2Z6OpAm0JuWQRcmmwjaMFlN2FhcD7wYiIfAdndAn63oLs9YY2nxeKMwGRqrUSHDFnR45WEvOfe9uoX8WWTmBUnU9V8qTxVyDVJq0RarPEZbDkCqLcr7Q+D59YGYFaEYpmQIuQPjMC5hXSCRJGguhaJl28kFYlHoVLkwpVCyiDJpdWxV1Rz3puW8y1wDZ+Fxvdo3ImP8Y/RLjnDp8T6ghMCnDefnFzRNy3q1RhtDnmOpHMmVXCilMA3KUrJiCqOM35BJRYmkeEqQMiVkDODaFVoZzlvNfn9gnK5fOzf4+CDlfidgvZI3lHhyKdx+9A1273+Fu1/7WfoPP8RXHaJJa/Yvb0jjM9oiKNXoepRKfPE9x6QNvXbCQS6FC+9ZN573rgrn54lHa7lHttiqSGzxRlN0Jh+ew3TLNF4T/IZw9g1U9xC9fpsxFRKK9uIBrlmxvnwHZRuKaWtnnKJUIvS8WlkSeh6TRUGpQc4bnqczBiiihRJHxmnkMOzpx579YY+zthI/ZTHIKTKNidvra0qGp0+foJQihInD4cAv/8ovY4yla1u61ZquaznbbHDOSYfnZyhRzZ0t77//Af/x5/8zMQvn6vmLD1l1DVornj1/ztOn32S3PzBMPbd3t0zThNEKZx3Pnj+XYP1kbGhjMM5VfpisuZXks8TcMWTiFNnterw1NNYR/YRrQY0jJmeG/Zah3y+dczOS9eo41Eox+g2TPudXX2652w7YuCWnQB96WjVxoQ489IULV/jgxY5dyqy+9JDVuuHxg3PuXrzg9vkTdBjQBEpJGOdQxt3bpYT3oliEtj7H8R0RoACy+FRoVyA9LRwRVXDWYIxka9MUGIbEbt9zduiZQhTeijaVPCVth9LSVXAq4H0RlKTqmBSTyVkRU14CFKOSlCoqUlF8g3Itul1TqtLqfmxIUdHHyGGE7SFSikGrRNsVSlbEHCkpEEMkp1QX4lL7/1+95lkkzVKwNbvJKBvIyhKV5jBFYszEM7B1lc51Ikwh0Y8TwxiF9R6i8BnGUVR5raFYi/ING9PglMZUeFDlRC6zuFyUhb/UCZFEuyTnBMZQsiYnJz48teMnTztynJiGrUCC0qgvLZUklC4YY0C5ewuJodAWiEoRtIYohExvGkzVhRlLZhdG4QzNZM4TmLmULGNlbmcumRnMMaoIiKIg60IkoUukxIgxhgZorKF1hkaJ+FveJ1SXKal64xhNUoqoFNkbSgSdoMRMHEap6Z4ZumhYaY2LYIo8xTIVQp/hDRucCAeeDPhXuBbCDilVyTeRY0+OgWkYhEegLdpvULYhZ2Hhx5iZYqIfAiEbQlFEqLL5CMFVS9moGjKcpMdKAnVmsb/jc5KFst5fjmCLfGMG+9USnMxff0togVdRlUq+rR9gtME5T9t2dF1H03ayaaWwEBBL1SEyylZkSeD6RdQqSxmqFFVJhhlyoShF4wTS1spJR5gxlWR4nyej7p0fx/twev6fUNMqFMb9Lf3dc4aXTwi7axGitA6ajvFuoD8MDP2EJmEvEqrVXF42ROMItkWlAhkuW8e6cVx0hU2TWFWtkJwUWluUdjJ3lKKEgRJHUhhRbk+ZImrVoybhI42pEKcDfnVO06zRPqK8JmtBUbIyUsadZQ0oZCUBSmEmS5bXr1vJs1OwtA8PUxVji4kQhSehtT56yVSy6jRODP3Abr/nbnuHUiLw9uz5M5xzrLo1Z1G8asZxWmTa4f4GXmPsNz6TkgvTNHF7e8uTJ0+JOVNUwehE1zpKidzc3bHbb+mHST5rGlAKGtegteFw2C9l2OUztUZbQ6meWDP+pGqQUtTME8mMU2AYJqZhRJpjPKoUotGEcSCGqYqVHpGqU3FQpRRozZQ1vfK83Beu7yJ67Mlp5DDtWeuJ7AaalaEtmrvDwG1MTDnSGcVqs2Y87CttRkrlxlq0LqD0UsKfOWKzKOmnDwXvH98xAYpCCS8E0CRyDvSHO2wTaS42nJ13vPOFt3j20TNevtjywfsfMOz3dA7Oz1Y8uupw+UBnt1i/w+U9lgmiyLorZTDK1fq+LKyuZHTOqJLRRT4ZLEU5sluJQZc/I9uOZFp0t8ZlS5MGsh1pR43Rhs63wncpmd3dLdN4qOUQMEvWkF+BRhW5aDIWZTuUsRStMI2lsw1JW+xqw+3TDwnDyN1uIGfN+tLgXcNmvaFtO6xrub7ZSt12Sqwaj7aO1WrF5dkFbn2Ga1dkFCEUyB5dCro4KEI2VWqClJeg36oMumAoKJXQYRCy3zBIIIVG5wmdM6somYj2nilO7MdRzPpSJGGARM4rZvjea8NV27CbBhIwxUgOGa0tFoWfFGTIQxJ1ogir1tF0He2mAwPX+xsCkSmOkvEng0Jq1NkW0EXKQcYwqiRaLodEM400zlFywDnFuvXorCkhEYcAuxF7BrYzhFJQJWEvPKvGcrm3pDGyGQ44e0Zjz7BjxvYBe5dAZQ4lig7E9Q7VWB6eHZ92ysKPkIVGJO9LVpQowWbKhVikxDbcPSP2O7YffZ1+d8fLZ0+ZpsQ4JR598cucPXib1cPvAbsiRAhR0wfDfsjcHgYJUvIs5S5ISyoa23hUzkKgTgWVZBwCLJoh8wZUyiLcpCj3EZRSM+gy18SPCMqrJb1PPo4lHaWNSIRvNqzWa7pujTGOMVQRtoWXFkX7Rim8UVirpG6GImOJJZHjAEpjlaVPkSkEbNE4Y+guHuB8g/Mt2jp2+z3TKB0k1IV4icM4okOn8/a1v1/J5BWCfOy2N9xcP+PlzXPCYYDmIVdf+j7e+p9+J8/ef8LNs+dsf+1XSPsbLv2WpoV3LgymbbDrDc4YnDasnVzrZh1xPtGkiZwVYwS8A39WOxO16OSgMdpB1qTDLXnYkW/e5/mHW263I9lf4dtzvvu3/05W55ecv/0Oum0wqwbjV2Bbiu1Am9lylYB0gpglon49s2+ahlwy2/2WYezphwMxTBLipVRRO9nIYxJvqBgDjx45Ls7PCGHi+vqaX/21X2G/3/Phs6c0vmGzPuPq4pKLzQXvvvM23lnWq9US5KjKJ5Rq6Zs7yaYw8uLlc548ecrXvvZ1Qo4Ulbm7/QjnjAQQYeJuuyPmRMx54UWWWqbZ7XZYY/HuuO3a1tGcrdjv9sSQxHtNS8v/7EEg3jeZ/jDgVOFs5Ug54lOGg6Zsb2pgnUhK3UMXj/MrSzKrLR9tX/Ji6rl+/nW2d1sYXxBi5OaQaHViZyOHleO6c3z9xYFtilx9/QMe9yOXD76Idg6Mo5SRHEeIol1VkBJwEk8XlII4JfY3iaZNuFfdOz7F8R0RoCwaCkUmtqk9tVYXrJHSf9taNucr9vuW/d6TUmAcDgyHLa2JlE1ElRGvRrSJWAfeKpyRerbWVmTipWwNSLatcpLFeJb3xQoJaanJCms6Fy16FcphXcFlRdOusFrT+gajCjlNpDiRwiT6ILBkWW+aOLr2oxvrRaWzFLT1GO/pAG0d/cub6s4bCE7kkCkF76yQrZyVFtgs7ZaubbBth21bjPcoY8goYhReQ0qQsxZi4xwpK1Al1zbvGsHXDgQpPZfKy0lI2KJRKYr+RcpSJ9YNumiRuEdIVikKWfHeYlZ3zDkLFqIj1SJAk6aq4hgkeyTVyt0CPogQWariZCULOjVnzNSygzMWrCywFEWJCUrVDEHQnVXXonIkGOFOhJAwScqMWs1tttJZ1jgpI6TYYLTDZY2OCWKpnTFV9yUkCIVi7y/gry2a5fhXqbelVGnrOPZMw57xsGXsd0z9nsNhYLcbyMpyt93zIDpse8GY14RJXGKnkBmmvAQocxacixBpha8sJ6LnDPnEJ+n/S95//Vq7pme94O9Jbxphpi+tVKsiDrBt9sYbxNE2wlvCSI1IQiAOEEhwZITECUICCY5AghOCBP8AnCIBQkhW2xLdLTdge5u92W3sCq61aoUvzTTCm57UB/czxpzzW6tMVdG0WtXv0rdmGHOO+YYn3Pd1X/d1vYkd3O3Bd2gKD7bwh5+WaOb7RIPl/bQ6cA0srnIoLTX8Y5v+IXhKSTr+VBlfSUp6KIM20iGhS2s0WgiW4vEFpWcbVbgsrqmp2vaOZVEyc5WS3Kd72evDchTHrz8jrnfvp6L3+HlmmIUTU3Ut1ekFJ+99kVk30K7x15fMOuCqnspmOiPkaKsiTiucVXQarAYTAzooVLASkCQLUUEQ2wa0FiK3MhjVyLjMXqQAcoBxT+onxm1kdgOb1UeE3RZLxHY1bt2h2xWqXqDbU7B18dMopZ+CBtyn0b55pBQJky98klm6UspdLGHwcYgcTFVTEX/c9z0xRl69ekU/9NxubmjqhpwzlbU4bdntdnRdR9/3OOdIJOETGouxBmvvtsT7HUAxJsZpZBwHhr5nTp5ExJp4FIYT8biZmAWFO7Tb+izljmmayC7hrH3AdTmM0Xzg7xSDqgP6cPgnrbuREALWaGKxA8lBHcfgkaSsyvfyIUyOMlcTzMOOYRhJ0w3a7yD2qBDxQcL0fQzUpfw5JUXImu3e03ZzKUFpjHWo4O+aOAo5FvRd/JkVKQo/tErf16Q+Hj8UAYpGnH5VFNGZRVOharh4tEJVNVOVOD1zvPfFJ3Sd5eR0SRx35Diwef1tGCwXrqOuMisX0I1BdS3OLNCmwdpWMnRnQKVCgDU4YwVgzvmo3KdiKhoRQYifeU9whmgsphYfoKauMMYWPxXp0Og3N+y2N4RpD8HLAktZqxVCor23SykF3WLBYr1guV4RM+yHkbptWa2WnBMgeew4sL+6ZLy+JseZ5abFuIpFU3GyaDlfLWjajqw077zzDl3bcrJaAKKPkpSiH0ZyEEl3in9QTDU5R2IqarYGWdy0PIcUA7vXN9JmHKLcP+uYA/iY6QdRPbQZ6qbm0TuPoXK0bU1KO7zf48eJlDwpLTmwO/3s2Vxesx92sshUDa6u6VYnaKW5ub3CR/GQUEpMJMMw008zwzSBUSQj1gE+ZCFTugbvM2HKhDGhMpwvlmgnxNYQI37y7LNHMbNwFlfXvPvuW/iceD5v2dlI2E3kyqEWhuS0EJdzgAyV0ZjK0ZlKOEobcS5OObF3Ca8y+ylgYmatWjr1kMVnNVSHzsiCSsi4UMeOnjyN5DCQphvSvMW5gFk6FtVTXr64ZL/t+T9+5T9wdXPLe1/9cVZnjzl//yeYVcd2bNhPmiFoYtKkpFBK0K4xBiKZSQdIGp0sKhkcmqDEODGpQ4nuEMR8frlG0AHuxva9iESRYVafCVDUg82cO+JfmQdKaVxlaRcNVe0wVhGJ4gxe1FGVlkqOjKeITpnNfsM8e0wlSOL546doV9GaqsiVR9RiSd00pBRQCqYQyMZSG0O9WPHo6TuMQ880DMzDIEHFNJLjwfzz8KweXsPDe/IwSNFIyS94zzjNvB4Smopnj97i9Ed+J1/5X36Gt2429JstX28Vu+98HXc5UDGyjoE87Al9D86QrGZWmaggVgpbW1w6QZma2tTi7TN6skGSumWLNjXarcnKQ75BxxETbjipNLqxfPrplnHc8o3nN9RdzaNn53QnHauLFe7sMXZ1xvrdr1Etz9CrC7COrOrDw+KOk3AvuM2ZaZ4Yx5F+7MX8LszCXymJjz4QmZV0nIQUicCu32OuL/ExoJTm9eUl0zyy6zd0bcfpei+mej7ywYcfst3umOYZ54TUXlUVi3bJyckJ6/W6IId3Ltxaw+w9m82G3X7DMO4Y5wGfAj4OuMqxWp0IejkNx1Z+gyH4RIpSegk+0LUdXdsen/c0jOyut1LiPpStdBLVaaNQ9+TrUwzEqAnzhEcc1Q+JWSpBIMd5oQ8xgsxL4jFR9lcvibc9y/6SJkxYq+nRXKoKn+B2hqATu5RI9YraGF5fQcqB3X4mRsVqvcZvA37ay/mFRI6TBEVKHxM+Wf4U6Q3S//d6/FAEKIenkrMskNZojDI0lUZXGuWgrQyLrmZatvh5JlfSXnV6UrHujJQBnJi8aSWEQq1blKqwtkFpgzaKzIG/oMnayFxTIjSWAIoGhTOKpBxRO3xK+DiyHy+J2ZBUDWgqayTyjEEcJH3pR/+8Rewwue8dWos6pwSnGmtrrGtwVUucd6QQ7vgIORKiZpo9lbG01rFcrjg/n1hiyNpwcnpKXTmqRhbo6KfCJZD23JSKzXaSxR+464JS0naai+dDCoYQNGEG38/k7FFMjCEzB8nUU8zUWtH6xMpnXO2o2iVROTAN09DjfURN9+r4xaGTJCiGsRZjLD4EJOc3UirIsg7mlIWKohB+j1JgNUlptGpRVBBrDvUM6TLKTLMihAzq4NAJRlus0YSiNeCcwaCprcNrJZCr1+SpnIdVMHn8ENn3CRsVBkfWmjxrQSQU5NqBNegc0SFjfS6aIHdHSlJqoZR4tD5kd4UjgiKUEkYqgnlR4C6U0dRtw8npCavrG8ZZtA7CuCVOt2gXWVeazlWcLCrp2gmRfT/gg3AvApCUJitRmlVkQaYKwpdVQboOJZ37Y/eNz+8PY8Udx1/lz477z5sHh24LQeskm3NWCJ9KSRaeskDOB8jcaE1Ck0oJI+cExqItGFdhnUOrIiiXVek+kwpIsjKWxZo+oXRgDrLgm6qhRmGMw1pH9IHJWsmeQ5CxQ/rcIOtwWGcf3pMcIXnC2OOHXswtraZZLmgWDU1jCBVEF7EmYHQQfZyUSFMU8nKEbDXJiKVFUgpqA14x1xFtI9oFUp5IZKKWNaAyDpNAVRFMImtF0pmoE9kalNX048hu6/Fxh9s6fOjpblv2uwXNtqc+2aLdgmYcsTGiqgbTrtDagqk5eJPdP3LO4itTDABTEk0fXVraD/8d7qE2Bp10IcVK67EqbvbDKDo+k5+pqopMPgpVDkPPxhguL1usM8QcWXQLjHYsSrn5MDIPxcsUE9M4ih/ObifnWCTk70z5YvFYuxNFiyne6zjK9MOuND/ddasd3uOw/hyI4+WucBSFK1yoFCLRewk4S3CitC4I1f0ApSDZuqAxWRo7ZGZGIKFzwuQESRcOXjqe+8HJ/dmTxyyWS1zsWS0aqgOnpHTpHU43c1dOOiA3OZUW9O8bFb07figClANHQ5W7VTlDbWDdGHRlqKwmJ8duvYAYMTrT6CWVhi8+W7KoNafNjDVQO6QMkTUZBxis61DKSP1TXNhEcRVFLvL3c5pJUWErQUeqqiWh8RiGzcCw3fHJq1uGKbA6eUzTdDx+/IwYAkO/x48DYZ5LB99DIhV8Xm1UFXIuBB/QtqJplzTtgrpZc7u/ZX97yzztiWEgpEgOik0/sHI167rj0ZNnLJZnUDdghEgsnRiB4IWdrZAxHoJ4Cg+TlIlCmDHW0LY1GIO2hhASMaQic6wIvmEcEpcvB6Y5MI6ewQfmkArJDJZVxepkxfrdxMlJx/L8HZpCaLt5/Qljv2ca9LGNNaXEPE6gET2DpkYbx3a7h5RZ1i06GyYfoJhVRS1ciN0sreTaihZOs15BMmRv0bF0kZhIVont7UxOQtDTRgvZrmupqoZxvoU0U9ct2mo615KzZ+kz82CoNpp66QRxu+0Zd56XL/eYbKjrDspiq6tKUJrzFtNUVJXDzBHnZ4x5ODV9gNkDJJQGd1SSPQSvmRnZ2FKUFvVpkvq91YblyQnd6oykNevTE6ZpRocd9C9oFgMXFxXtwrJcN4x9zzTOfOuDT7ndjVzTMqsKY9ZS7ikOzyKfXRZaZbgvnvbdQo37WMH94pBA2/nNGPwzc+D+R6PFUK7txCSudhZyJswzIUHOmra1GGWonCHGTAymbASaqrG4Buq6w1qHMaasJbIxyvekQ2McRQhwnidCzGg3oZTBVh1Ns0ArhZ9FhmC/2xKDx89TMWDzR6f1hyiQrFvVmz2vaYYwMNxesb+5YvYRW1ecPLpgtW5pnGdmw+xfo/01OmywBHSMjLuZFBLel01ZK5KRpoHUNMRKo4LHOKg6TcgTMcOQIKLpYsZ1QuzXVUa3maDAA7EypNryerPl8vWW3X5CK+g+0XRdxcmqYfXoMYvTM/wwsrx4RPf0bVy3pHv8DNusyN0FSlWg7IMRkHJiGKVVOKZY2nEzWkm7uCot7gcNHWcNKRnpWBpHpoI2Ka0Zx5EQA9Ps6VrxFaudpa0su80tQ7+nH3uU1oQ0c3HxmKpqWa3WD8epkg14nmdubm/5xje/zvOXLxim8Q7zK+Ub72dR5w73SjLB44HgJ3KOTL7Hp5n38/v3ovZcJJ7L7UgJ7vm+HcTPSInkA9GAH0cpkedYPLDEloBit6IKFxOlUJWThDF6MXTVRjh2VosQYFKEZAgegvcECuoSBaX6sd/1o3z5/fdQV59ic2CpYHNUNiqlTaVIinst1IVgXrSTDganP8jxwxGgcCj3SuYjrO+RzeVLjHOwOEUFx0JXpApUa1lUMmjPT9Z0lWFZeYwWVvahhhhTqUUrIdnlki3eqfTJ39cIqVIrVaJ4XR6MZCTLTl4fxpHaaaoaKpcxeGIS7Y8cvARYh6F/vyPgyMx+ULDHp8AcZ5KfMEDjalSOqBzQOaKUmOYZZ1mdXeDqhrOnz2gXS+rVOcrN1F3ANC3aivsxORODtGNPQy+ZZkrMzYSfPePkxZBKWVAGdIWIXh0GfmaOIzFnqCpyCERbMU6R69ETCqHTaoXRiqapqBsnHBqFvI6RyZQN6RCF3zuEXyATmQLHei8Ls4nSbaKyoFrzHMA6UTSsOulCyhaVDWl20s0UPNFnYshHw62p70kh4GcvipXOkU8VWllyNijl2A1B4N8c8Sni54BKmhpNFcG6RO0NDmjbjhwzcy4ojoJKaRyaPEhJIKuSrelIpR5CopPP9GPCOTAobFENkQVSUK3bmyu2t6/5rW9+nf3mhtvLVwTv5Rq0w9iKfd8zTzPWaKxyjPsbSDNNramqmXPnuPKvmDbXvPzm/87Lyxt6fYKqlyyefAlrW1y1xjYtxjZsZssYNVuvSgfb53Mq7h9Hj8ryT1Pk938bbgJw7JQ5HNbKc2nqSj4v/kQohY4Haf9IihCDBEFVXR/RjkP2Z5Q5zrGjl0jpig2F5xSCZMgxylmH2aONKPGmsiz4EKSlWym0tVRaic9JgIMFvTbyt+47NCv98JrTPBKHW9LmEnbXLK1i6TRLk9G7K4Zv/7949c1vcPnJR+w/+Tbz7SvqMv7nCVJBLq1WR96QUYrkwVZgjCLYhJ96kspknYnakrRh3N4SwoxrWmxnqWpFVoZkW3RdY6NGNYrkMjf9Xsq0W03XOHbbifPgOJ3h9PGldJikiO06Zj9iu1PcaaBqV9iqPbYLy+0TGwIfDNFPpFhI4eYeWoZ0+GQyISdCDIJeJ9HxGKfxKMpmraVddpycrFmfn9CuWmxr2W62eB/Yb29xlWN5dso0jdzsbgmfRi4311ycntE2rTzbFNlsN1zdvmaz34CDk0enGGfRVkxmrTW4SlDYUx+wxuKMJRUk8sWLj+iHiWHqMWXc3Y3pA/JfuCIF+T2g9IKQ3HH7FIfgNknJHciq+F4dxBKVIikhJ5tDZ1AGnzL7mLgaPS/7kWmKxDkTVRAVbJ2ER56QjsXKsFrUnK5bmCryHBk3l/SbLdvbG5SfRKG8cLUOnJnjHqZ1SeTviPDf7/FDEaCAEI1MYbCmEPBx4vb1FussbYgo07HUpyinsK1jtehom4rzk1PaytLZBARSnokp4KNHiQRCufGJgyARlNJMgbQSGW2ETKm0LvwRCVCsc7A01E3FOPbUlQarMDZhlcfHmTgJx+MNOuiDjFGVTe1Y6gB8CkzFN6FCodokAUr0EqQQ0VZjasequaBZrHj0zvtiVFe3VG0kx0zddljnsE6ymlB8Lfp+L06ZIeC9fO/V1bVoimgH2kqAojQJU+rYWYzMAKoKQiRVFcN+4mbySA1AszQKYzVtW9G0FdqqIqJWiMYlQIlvuPqi1NHM7Xg3chaNjln6eZXWKKclk5wCJIOyDtetUaZmnuRX46yZZ08/zKSYHziC7m/3RO+ZhwltDLZyaO2wrsFUGqUdu36miE0whYgfA1WuqJXFzWBsps2WCkPXWlkY+724lBqFURarEqkfSbMn1ZZMZlKR9s0AZc70Y6bTcp+qQ5bCIajy3Nxc8erlJ3z9G7/JzeUlVy9eMAwDtzcbXN1Qtx2L5ZKmrjldLlAaxt0VhD2LKuCWiTPb0c+fcrv5hBdf/yW+8/ELQv2IbnXBeQ2LxZpVfU7TnFJ1a170LdvZMQTJbg9Z3G93qHsfDxS+lBGi9Zs/ewwa8mc+d85SOScdcEZjjComihpdxOViisScUD5jrC1KsgvqZiG8nSxk1BxTQQ3lpDIQY5ZSWYHqBc7PQMLPAWsVCpHyT0qceEPwYpqoDbay5GiIUyqE3CiKpsWV93AtnynnzQOx3xA3l7CVAGVdaZY6Y7aX7L/1n3n5f/wnPvnWNxmffwzjnlxHUswMI6SgSLMlaemaiakE7B5cpXBW5AiinlA2omyGqiZby7i5wcwjTdcCLdVqCdaSTYdqnFxta8hV5mbYM42R7BWttdxWjpAsKcCjJ5foFJmmPaZpGMceu36EC5rlSaBZRlIMD+a1dQ49l+CkoJ+laiilOqUIwROSJASH7qxUNuwYE1obVsslVV2zPDlhdbJkdbamWTa41rD5dMP2dkeTI13X0a6WTPPE1eaa17dXZOBrX/wyF6dnuKoipsCrqxe8vnnNbb8Bpzh9ek7VNUeRNmtNSVA1VlvaqmXRLNjfbul3O15fvcBvA5vdFmerz9msD4MuH786BCdaSTn6KEAqEQp3UbR8UCpLJwBALu3JSsu3yr85ZTYxczl6XuxH5ikQfQKNSAqostelhNUVTWVYL2tOVjX0NVMe2Tx/zf52y/b2mtZkFlYfg5R04J9lQU6UES2kA0D0gxw/HAFKysWoboY0keOMijNGB0xKGL9BpwljZrRONHVm0WjqJuPMiNa2cCoSKc346PHRE8KhpiioiTEVShtskaMX91MJOY2RiPUgM5mLV48m0TpN6yzhYs04e8YgfA2dAzoJ+VQjrXb3qWMP+uV5iCQowDWOqnWi7Bozc69g3hJ7yzTdEsJIs1zSLVa899ZXabsVi4vHyLKlBIRIMPQTfR8Yh63Aq0GUc+dppHLSFjePe4ZhYLfdEWPk/S9+kbbtWCxXRblxYvYDIcx4Z9A5yCJOZtE0zJ2n79qiMZE5P6lpakPXarSaefXqY+ppxz4HjGnQpmYeB1IQrZgHmbUWwtnsJxamwhlHXVXorJiGSRR5TU1Glbp3A6YlJEtGC8ciRvy0Z5pGhn4vTswxkoKQ0nw/CFLkA0lr0mzoG4u1ivbsBFfX9IOoqdXWYLXhvOsEVRo0ykeUSVStozWGxye1sO+tw2fPnD1dXVG5ih2akBO7mxsMcG4rtL7LLjPgI8wROmXKYq2Fi5KREDl5ri5f8OknHzLsd8ToqeoGHxI+ZuZhYDtN3O73WGu5Xa9x1orJpNZ0teX68guoFLh+/Yrbm1v6aaKfJ15ffoemvqRTmbP1gvrxCc1b71Gbt6ntF5n1GiYDUR/h3M8XVzuM5LvrEg5KxmhQ2XyGf3XYxA+fHzPJoieRUiREL3B1PrgvO6wTddt+8IQQ2U97jLXUGZSpqWrQxkkJxAdiSmKEVngE9zVyKDwAcsYaI8RqIbsQ5wlbi0qpURmhYElGW1UVKWp0lvE2zeKPddhsQUwkjXkY1I03r9l9OhFff4LZvOZppWkJ5OcfcnP9CbcfWJ5/+BHXL1/RpIArXjApCwIZosIHzZwThoxLRSwvR+FvqR5tM9olkTO2iXqhMdgi/BnoX92Qxlk4ebWibR0uJSoV+dJbHSsbuH5+yW47s79JuADEyPblhtjPdMsl+37my//jT9Ctz1g+eZdcd0RriCkwz+MDBCWlRL/f0g89kz9YZEh5JaXEIkYeWATEg2lfEhE+FMrIOjGPET+N9HvP1WsxGjRaYYxioWoaa3n87BTXOF7vn/NyfEF8/XVikOTkw0+/wWKxQGsJcHfDns1uy3W4JeiAahJRDeQ4Eve9IAiU6EELPSBlw/7VLcPtnpevXjEMe/wcikT+vaWsJJAmScEkhARGo6zMc4MqZripCDnfJcHSWaqPppf39YCkoJDJcaZsSKSsmLNBaUfjKnQ9ExRsggQnTa0JITFNkbatWJ8smactm5sXxM1r5u2G/fVrpl1PHnt0bTHWoYN4relDwFTO5k6j7dhj9H0fPxQBisBdQWq3aUYlUWNVWTRKiCJDr3NAKYMzmtb5YqIXOHhqSHdKvMuYopBBY5SHrpQ0V2pTHD4VxxYrjSi5ig+ToCoiYZUwWha1ZdeIau0QCBH8HEqNukiTa31cEKEs5eqwpL8BfSvK4FTHiRVmyF4W/JBHYprFudVWnF08pm6XuMWamDLeR3SSIGWee4Z+4vZ2Q4ieHIPwTPzEsmvRixY/zczDxDzNoGC9OqFbLFmu1/TDCLpHjRKfVc6hUiDkXL62NFVFV9fS2ZAyi66irQ3WCkS5390wk0h1jXMLrG3J00SK8UH4fdicUiHVSduiEidmmxhS5OAyLWS6gvLoipB0IRAmgg8M/VAClF6QoiSCbDkm0uilHpwkG0hB4ceBcaip1ydkLD7KxHdlsehcTciZ4AujVSt0Jd1ei6aTVt4QmKJk+JWRsaiSjKNpGHAoaDXqjYUsxkwISMnxOC7uCKc5J8ZRpLBjuS/G2AK9ihibj5FxntFaAlRrLTnMaBI2J2pnefb4gn63YxzE8CvlzG67YR4GNi8aqqnDmxVpZWBdoZtnGCtS8dzz5vluKp1lJh2DE6GyHn4nfYaDch8xuTcICvohXQ8xFu+cElToks3mDGoQ4vk8zZiYULaiDkIktaUke9joxASuFJ2OUEqB38s/fSjlgvACcoTK3rMgOJRlBSZXQLbCEQgpoUK484bRdyTe+4fvtww3M2l3gx52LI2mypF0e81EZIiB21fX9Lc76oWghUdOS3FC91FKbjpJ2UBoqaUzqZ8wTuFyiXBDpqqQTkEk8Zp3PUYp4n7GZFeuRUQLH60qdGg4Wzq0TwTt0RGUz/h+og+B26sN2lXk7DCuo16ckaxjvkdkfvighccRvPh5HcZJLPcs3euqkWisjI2DtbQ6nL8m+Ewm4MNEVpF8I4h4yokvP3mGWy3pWouqFK+mDYOf2M174hxJIbHpb6nrVtCznJjCTEiJKYTSOZPLPpGZvYwTUwKUqDWzzwxTZv/qhvF6z363IwQJfvMb7bbaaKyzmHzogIsi11CC2AOh4CBZgFbSDl7IsVrdSwrKDBOk8cB1SuVOWlJWhEIAt8aANWIyG0RjxlgZR0ZDVVm6piKFiXHY4oc9ftwzD3vCOIKfwcl4UYWVLevR4fqK/9chOPmchOV7OX4oAhSVJvR8jQm3mLjFpgmbkyASITPttkJwjQnbLrBNR2vXNI3BElExEUukHkvtUwCVhClZUkqJfdqQs0g6W1dR1c2BJsScZXF1VSVKssZjsigIzj6Sk0IXboFJM/OcuL70zLNkuAcOzTEQybKEq2NU/PDIOTPur8nJkGaDwYB16BwZicxxJOTAojvFas00zoT5lv6Tj+mHgc3tln7vGfrAy5eX7PcDm+1GBNKiJ4SJedjz6GzNo/MTKi11bDcHcRDd9UzDzKcvXlO1LfViwdmTt2gqy/7mNUO/44Nv/RYp9xjjWS8XNM6hlAQtTQNKJ242V4SDdH3KRB9R2hWuh7Dq6+ZdMXVEiJFd0wm/RIWyKEearsJVhjBPaGtoT1pysqRkGOeGOVQMu4nZB/rNjRheDeIr5P0scvcpkos+i2i4HNj2Upaa/YAeHMskZaR2sUKliEkBlRTKI4FFiGQVSSqyDwlqy1yt0MayXq4ZBlHo9LuZIU3sVw2TQhydY+R29LjcwcnhYcN+mLndjlQuE2qDM46swJJRWWNNzZfe/52cLB6Thl/i6vULPnrxXxinHXU90KDQ5ee0rlHWkbJiN4mIVJgm6k+uaZrvcHa6Ztmd8aUv/gjL1RN8+CZ+9uyHHmMz1Y3mylxh9zXh7S8Tu0DUS5QydwvU3Tp1r6TzEAGUHyuFHiXn96Ze8uchMbpg3T4GCRDGTFU5WtvJBh05lmNCIVynrCApgk/4aWLqdyQvrf6bmxvZHKO0w9dNx1HXIUZyDqg4FTt5R0yaGDPKCEExkUtZx2JMmbdKl1Kl1PaTtmRl8DGhY1GQ1uZYsrx/3N6+5uXs6a8u8ZstiyhGbNvLG7Zz5Gqa2fYj45yotbRyOslzCtImpo/EBCEx5YwB5jmilcf1I1XjWKVWNnWr2cUJbMB2FRhF3I7MuxEdEq61VAuDqQLaJp4Yw/rE0X/lMZeXA9/012RvUL7CWNBW4Td7bjN84//8Ouc3A7/r7a/QtgtOnzyStmMt/jjHZ2oM6+UCo5Xo+EXhvlltMFoUoXMSAj7l32G1rKsWY2tctUApU0pzYokXkpeOnsEzTAPX9S0Kz6vdiqTh21cvmYIQap1xOG2JSWHshv1W2pNDlPU7ZTkvFRIxCBcvenEerpoKbQ22dkyzZxgnhu3IPAhhPaX4gCR9OLrVgvNnFxgrKEg/iN2CsAiSICCqdGxaQQiNNShnwGrRrymBpb6HoKhSNlSFg5SzIvrIdl+SzBh4tqww2ZLzwBASWx+oc+ZxrXmrgbfaTK1GUrCMo2gq7ceZcfakICW6mCwpFTk+BerQdUQJjhSCjv7/M4JCjpBGVOxRsccUt1eJ+tORhKRyxKiMM0LQNJIuSB26ZE+xsJcPEtcpRcIsUb2PImUfYsbVgpFYLf35qgQoqfSzx1xyQyXdPSkpTCUMKEWGlIrrJhwcQQ9kJo7Byh3/5AF0V47oZ4LRZC+E1ZQziYjKnhAnQgqkWpxB/TzhE2yupVXu8vKa7XZmv/O8vrwSIa+hL+Jnh9bnkaE2DI0FZ3Fa2rc1iv12R8hwO050qxXLlKVFuaqo2yVKadpuSU4w9kPRfJH6tzEKV4l40HawJF/ErWLATyMqz0gZKoMyuCphjmV6aSaWzdAU2f541GIxzhRTOEfKFpIlByNZ5SRdGNOwL10WwrzPMYjcfo5lQ0oypjggFvKZBLCCrOWUjpynWLKHHKWLKcUISpQmg0rMGcbZY60iq0K0TIoUynumRNKy8WrueBb3Dx8So48Mk5xX5y1WH7IkBcnQdKesThXrkyf4KVI3CxGTijtUzOiYcCqjlShxpozYIITE7BO7fuLqZktdN9R1S9t0rJeBk9WKaRxRyZMyTCETPdhZNkfRTCmkvvvGYOrBh88NtA88lGMb6ecDL5+ZB1KKT6SsJbCI5qioG6N4gKSDoiUi5HbXRiydatFnkhLvlhDCcdppo8lRukhSFgKy/EvS9YUI15lsj2jeIbu/IzPeKfFmJCPWxkKYhdgcZd1RJomL9r1j9hODGvGzJ/pIwpHRhKiYQmacU9ETEi0fSyaqXFrRZe2JpeaTEwXel+duQGwkfCL6hMoKnZWM+QjaiRFkyFJ2nrY9yWsIGtdETJWpFx1KKc6XNWlKLGpDwBCjPq6HNgMhsru+wTUd/c0ttmlYWweuImknDrj3xkFlHd456uBEJC4mMezU+si9uG/roqC8brHGoTgYkR4IyEoE6bIpthdaEpRp4mbXk3Riv98zR3HszpUgntEHUkxsb/fyDEIENFpZUVIu5cAUInEO5dzAOJFciN4TgyfnUEQYZSx93tC2zlK1tfD/FARFUYxOxZhW1hi0WAgkJa3yuaw93JsX3z1AORDGU+FJBWJIuMpQaUPnZEzufMCQqZWiIuNyJE0TfjR4PxG8Z46pyCwU+kPOpbtV383d+0iXuj/Lv//jhyJAyXEiTVfkvEHlnrrpqK2jXSxK3dKjtcIYjWuXuKZD2ZoYpfsjp0yYvCD6ociGp8w4jng/sR96gRpdRcwwxYTtI9aMnK0XLNsG5wyFG0uMkf0wkfJIiDu0smhlaBZFGyO70p5emEnf5ThYhR8GoEBmh4uGeZ5kE48Oqwy2TqW05Yl+JKWZMFRMPnCZP8H7wPPnn3B1veGjT1+y3wfGQbQHUk5CBnWWk9NT1ssFTx+fs2wqlk3F3A/SEeIT4zzxq7/2n9gPI1fbHVXb0i6XvPfuuzy6eMxP/PiPcnr6jK9+rWJze8O3yUfV3vV6Sde1dF0rMHhVMQw9t7c3Ir7kAz6IjkMIEZShW6WDCS8xJfrRE6LC2obZR6bgi+8SVK3DVhXVomUOmtkbxiCb7+5mwzwODLubItFfMvRDKadkSGVVp6Qux8UgFg2Wqd+XhVGRY6S/vYEY0SXYJYOxBm00qjWEQfPt+BxUIuQJq6XsU5uKxho280xWidO2w6CoraFtFw/GwnZK0AdxFq0Mc3ZUVtE64TORM6F6jDs950f/pyX99pazd77Kq9cf85vf+FX6m5f0l8/xfoeKOyZVE7Kh90ImDRGuNj3xO58yzoHNrkclET38iR/9EuM0cXl9LRwebVmunrJ+8hV29SmzqjHaFg2azx/Hn/fxOH8P7TSfx1u597PH3y/PJieRARcvpUhOIykaUtA4V6O1xdlGXMWrw/h2VM6iMnjvS4CYhTNS12I2aIyognov7f9+OkLzOQkhf/SRqqpo6Zgng+JgYncoR2X8LNlwzAlrHYvFgjBpop/x80jKmckHmrpm0dbH6xx84DZP9B6Ct0TVoqoOvXjM5EZQe0K8YfKRTR+YVKaq5M77EAlBEi0VFSoqAgWZKgFqozUEGLYTqmTjdVdLAnIIluaID5FdDBgTMTbQLDR1Y+B0jXIVjxYVyic+WmhufeSmD6wWNYu64unpmrqtufzoQ15dvuI/jSPv/A8/weLiCWbtUJ1oQR0OozXLusUqQ1PXJC8opjzyjC0de6a0EoQMrrK42qFMA1RsNhtZy5K4pjvXoZTYiay6E7p2ST/39Fc9N+PX0VZhG/EgillTOy0SBKpCJbj6+Jr9ds88TlTGseqWaGtQTjMOg4yfOWCNYXm6xFWGujboqka1Btc4/BjYXW0I01xQtodj31WObtlinJX13gkfUlraPQFFxBC1Y680IWuqZEjZUGuRd3DOHAPwu6kke4XSRZG86K2M48xmP7LpJy6Wj7CN48mJZhxntN+K51tWTPuRzetrXlrFblGjw4yfZjaTcLpyGe9h9mgtyukaI3QXLaVvHjR2fPd97rc7figCFJUDKo/oPKMJGCUZt3NSg0cXwpGxaFtJ5p0Ewg1JEWNm2I9i8e3jca0cx4F5ntnsRmYfGeNISDD6ROUUTWUwWXJ9rSqUKyQ/JRBjP8xsthMKGUCrucY1FteuBC4sD/D+wp3uLdJ3GePnHQJfoxIqZZSxpGwgRXL0UrKIURRtlT9246TCr/HzTCi8BDRYY1mtT2i6lmfPnrHoOi7O1pgsdWeXRNCqM45mmvn08go1e4HPx5GQEq9fvibMgSdnZ5yerGlqTV03nJ6dMQ4V1inaRUfT1LRdh1awXq6wWjEPO7wXNCNmgXBzTHcpQDlijOzHSa4j5WOAPmePApq6IWnFHKU1dxwj0+TxUyBFL5yfw6aWC4/hkHHlw2b5cKM8xJIczquIJaUsWc7Y91IOzEmyOKWIQaOMBgzRaVylUFog52QzMp0jOcMcR2YSravE76WuxKr9+KRhDKDmTCQyRlCbmcpquko4JpLRiOmhp2WyGbV4BzsbmrMbvDew2RP9nhwnPIaYkwQ3SoSvMjD6wH4Ycc7SVRprNGdna3wIYGCcPLu+lENDQGVpexb34gOK8sb8/Jwg4zP3VyGL43cd72/8fs7HnDRniFGyQ6NnFKKbY4w9Ck8pfUBRS6kgxSPC4VxFBinZlk6hGEVSPBS5cvl7cqLxQM4sAl0pBKLRBRVJx/F6MBE8WDIcM10tXT7kg+z+w2vUtsJWSXhT2mGqBdXqlPUX3sdtt+irS4Z5EkGyEMhk5qRK47k6NP0f7650VhxQ2gL3x1yiLeEQGGvkd60HJYrNSgMpYGzC2CAbUNQYO2CqiKobrEq0jWFvEzF7pqBQc2aYPNkYdM5kP3Pz/BNWT5+yv76mq1rqbv3gmg+oksqi25O0km63A2RymK9FF0SUVw26lHRiFsQ3B49WRXI9GjHJVBZQaDQaQyIzzTM6ZhHRMwZMCXt1WRPiYY2QkkUkMo8zupJy+mHNyClLK/RBlOqgV1g4IuoolJbF8uONtaVuatR6LSUerai8iAzGFPEh0AwzwQeCDzidsRqUM0SjGFHE4hBtlRJO1RG1lOBPyNFynTFlIc0Xg9Dei9mlQiwO1o072oNkpRlCZlf2xVrJfD9QaLS+1/J8XD/zcbyVcP7QaPS5pdrv5fi+A5R/9+/+HX/v7/09fuVXfoVPP/2Uf/Ev/gV/9I/+UUAykr/xN/4G/+bf/Bu+9a1vcXJyws/8zM/wd//u3+Xtt98+vsfV1RV/+S//Zf7Vv/pXaK35E3/iT/AP/sE/YLlc/kAXobPHpg1WjVg1Y3WLs5m2qTCuQleiDquNw0fZlGc/ETLEqJnmyMsXV8zzzDSPRd9AMY+J4BOvXo1s9zPf/njDNCf6KbNaaM5OLOPXHjO/fcpbz87oVE3dVCSliREuLzf8xm98xMHN8dnbJyxPWp69+z6o6p7QVgGty+jKB5XW34ZwmIFxnNDeY7UhW0dTKWLwhFkk4slRHC6zJtYecqKyFqu1GP4hOg5V11C3Nb/jx36Mi/NH/OiP/ThGKXLwXL16wdXrF1SLGmstb7/1LjFGdj5Sv3rF9WaHD4F+u+Xbmx1aaXZX11xcXPBTP/W76bqGL3/1dzAMOzabK1mclWLZtlilcNnT7xoYbhmGCWYJIkLMqHAIUO4G9+g9L25v0CUzahuHs4Z+mEAl9LIRSYA+MuwT+21ifzsw9TM5TigCusQ8vpBJ7xMiDzDyQejo+G2kno+NxGkiaMPY94Kw3d6ic6YyWtoNtWz+KWfqxlBV9iDFQqYQe7UiRAmqbvorfI6sz86wXcfFosOZ6sHzvp4ym13GmohRkY+vZ5wzLGrHoS6YDu8dIceOufsxkvoCZ7xL0v8b211iGL+JDzvpNsGAFp6UqxpQiiklXm+27KaJty6WrBct737hLbTWPNpe8PLlJd/85gfM/ZbN1Suq5YTrhHCa1KH89nC8/ldt7Y+Q8G/fonxsNT7OAQkqUxZrhRBCaeXuaeoWY8RnSutCADQHjkx5zsahtWGxWojWTVUTgme33TDNM8MwkOMsgX5peRXIvoyLEIh+Zla5lAjlzMSFWwlxPSV8lG4/nXOhMwlaA5mYRBDu/tEtTlkvFZ80a0I9Up0/pXv3C/zIH/xf2X/yKde/8RvMIbDf9+xnj0qJtdVUSsiwClAq3glo5UPMdCCJyzYSUirFKkGOXR3wkyerzDDNKJ2xNeIdVmXSqJkrxTR4bG1pHp1iU+bitKbvA56JcZhIo8LXjsVywdOLU1KMPP+N/4KpG57+6G/ylmtZnD95EMjmnJjHkZjiEU2gqBNn8pEHmGLhFc1e/GBwTNPMNAfivEHnmaaS8vocPDE7Yq7Ft8xYnKtAO6bg8SEyDTO2dtRL0fFAwxQ8KSS0M9jaQhLy7G6/wwSLTXJ+ymqpyWiFcgrlIOmCvCZ1dBcPZRwADxJQgPXZGfVpc68zR5cNQQirIaUjD2YcJVjpdz3Be66mHpsyXczUQKuhKr5LB+TdRi9WHXMUJNkn+jkwTJ4X257NHOicozUV75wpchQ9qF2GSw/zVU+t4awR5SWyoPrG6lJ2vwsYszrsYYWZWZ5XSiXZ/AGO7ztA2e/3/ORP/iR/4S/8Bf74H//jD17r+55f/dVf5W/+zb/JT/7kT3J9fc1f+St/hT/yR/4Iv/zLv3z8uT/7Z/8sn376KT//8z+P954//+f/PH/pL/0l/vk//+c/0EUoFbHMGMkLC9x/byCU2l3OipAghCxQaMokLDmDrVqMq2lXy9JNkJncjJ8j3htcVbHdB8Yp4HpPXYHKiWma2e97vF8Soy1Ks9JVQlbMky/y8IlxqqkmLdmJ0aBkIRMwpWRXSoy7DnLe3/XIYvCmspfMnMw0azFDy6K3YLQV51Vbi0osYI2iqSzrRUPbWEI2nDx5TLda8eUvf41usWD2if1uy4uPP6bfbRh2W+rKUdcVb737PlXd8PjpU+YQxUiNUHTYBRHabG4hJz7++CNOT094+52nNO0CVzkOwm8mRYge/IxJgZOmptGa2lh2Q2A/RvbDTMhvbHhKi/ujUqAMxjqRAW9aooqMMZCjJvrIPGb8mAlzIMwThFlk4Atn4JgF3Vs07vPhJfU+8Bjk6xwTYZpRKMI8iTBXLIs8hxp55lB5DgGUyozDJBBwIwsl1pClLE5FjU5RBLNSYBx7cBVVc3fZU8jgEyYcau9gZujnuwGRimNvLp1SPmhS0Hizxi4ec/boC5jpijFsmb34AAWVZCOyqgjgGXKOzNPIzU1kHPZFwFAzh8Dmdss09fjdDSo1rB7d4upTTLeSjqF8hxR8z0fOUktPn2/zID/y5mv3F/o7ifKURF5+ngWl8EHGjymdEcYYtBXRunaxxNVNMdy0YrqidDEHLRLi4a4ElUHO8+6vEYI/Sv8f26GTLe2g5l7wVWTRY8n+C93mIM9+/2i6BcuTGl3VJOPIVoNRKCNto2G/J06eFDPbIO95WmVarVgrI2apOhM1oPO98pTozcxZialq2TOUyoRJOiG1KsaIHtF2iYns5BbrnMhFyijFiNn3ZKVZtorzs4a339XMVHjlaBYn6KoioIv3USLs99x++inn730Rgn+IVOYiuCakPKm6HqTuC0xamD2S2GkNWSMWYdLBuagjTie6Vp5dRNNPis0g3VY+iFaWMnJtkcyoPCYk/E6sNurGQdTCJUvHGwRIR9HBIV0E9/SxEnyItrKi2CzcoQtHBCXfPYvDYV1Nre8JDB7djIWTkpUSlCYLdy3GzDROxBCZpgFigDhDisI5TLNoYZHQKVEnQ0zgiYRcyN3a4lxDjAofMlWrWVSWZ8s1w+i53vSYDE6BqzTGwD7IGh/HGasyC6uwhcupVelYzWLpkNCl8y3e3ZofrMLz/QcoP/uzP8vP/uzPfu5rJycn/PzP//yD7/3jf/yP+b2/9/fy4Ycf8oUvfIFf//Vf59/+23/Lf/yP/5Gf+qmfAuAf/aN/xB/+w3+Yv//3//4DpOV7PTQJq2csHkNAKwlSDotYkuZgyIoQJTiZZy9RrdakrKm7pYi6dQ1kedBjPeCnGWdrVoM4zfbDTLcdy0IYmMeJ7UYxzRN1dELQw0hWlDXzJCJnIXhpUW008ywy8UrZY736cNyhJvfXtnyvlnd3eO8hzihnyEWYzRSoz9aNEFbrFmdqtLVALJoXjov1EmVblG15+ytfY33xiC999UfIKD748GM+/vgFv/LvfxVNwiho25pu0fHjP2FYLpa89e57jF5aJlUGknTnaJW5vbli6HcsFjVPnz7hrbef0nVLukVL8DMxeMabK6nRTyMmzJx1NampiSvFzW5m089c6T1jSLLgHh+2gsoVKNtgbUVVWVQV8TlwFUZp6RsqwqAIg8KPgioR5+IjFI/ByUGXhVIweGiomzlgmgolBLkY8YPos4TghRCbDoRKpISAKQiF8Ghy1vT9QJMrFosO4wyqdmSbSDpTuxaXJcjx2dPvNqiuYXEvQBlDkk2Eu7kuFmHxDnkjiRotIzlnfLSYrKjtGrd+i0dpi95/wm6+pt9uCLF4cuiEc3fozzyLj8nr3qNIvHr+CVprqspJq/Q848MlsU/ot67oulPc8ilYTTyu6d9bkPJA60d9drO+O+7KOQ9mgrqvvSBkT5UUUwzHjeXwjoeNzrgK42qUsRjniguy8MuUvuuUMEaeYzqOgfL/guZDIsYZNQvB+3hNtkiQW4cU84Swm0rZKMcosZDSaPvZ+9QuV5ycn6DrluwcyWqyAUUgzQPTzQ1hnIgxczsnppg4rTPJak6sQamI1hmlE16LznMq7cYxZUYUJmUpT2e5Fp8D0WucrlBGOtJSzvgUSU6Rg4KQSU4owtYrdCVS6uvFgmw79OqUXq0YVccUHDnBPO0hzTgScb/j6sMPeOtrP4JI3t5l1Zl89C8KPnDY9UUPRAoW6e67IiuQFdOcSMGj0sh6EemqzGolz04Zy9VWMfrMMHt8FM8olSBVmaASfZ7Bg9mNVJWlqSsh3kfhIsoTF/mHlIRITOBYNpRzl+AiK0VpMTg6LB8ciinjKN3BWQA4V9M03d0QL0FKVcQHtTkIoQkZWMTqim7MNDLPM7uduBP3fU+c9iQ/QhxRKbJIkLNiRlzKY8poU1FVFSGB8lA7w7preO+tp1zd7rkdElZnag31wmG14vr1hjhJi/rCKU4rizXS9CAVgFzWo8OTknsAYrfw/7NdPLe3tyilOD09BeCXfumXOD09PQYnAD/zMz+D1pp//+//PX/sj/2x7/tvKDK2dOiYki3pkjGhLVrXkhlij211ZItKUC3OMLbhtDnF2oq6bgh+xM8Dxl0x9nuU2dF0M66SxXu/3zPP4o7bto6qdoxzxgyRqlVYZ1ksWt5++xHBf5lx7JnnkfVJR91UVFr0ESpzyAwlUz/W7bhXdjgsi28szJlMiDM5TxhTgdKiy1EywOTFon3wN0zaMNsBlRVZJ2xtWK2XKF2jdcWisbROMe03jNPMxx98i08/+ojr1y9QOaHJrE5PxHm376nqhqpp6JZLTs8u2JkNKRza/ko/UkrcXF1hVOaTDz/g9HTN48fnRfclMu1umfY7xn1PjjONUVJ+chXW1izaSNNUjD5ya2WjADDW0i0WWKVxyrBsayqrue0HYspMIYs/YlCEOTGPQuTKIRRmfSwZSVl87leQShCYtMIUN9ASe6DgyDkIfiZnMXDLypCMJicxZLRKY60+Bjmi0RGLD4yojSancSiSc+RK0aoKozKdUZgYSUMhP987MkVRMsuCqArnQ6uD5sHBCCyRVCBrMXoTA0eNaxasTh+Tzx9RhddYxCtmLlB09APKOLA1Oh9k96V7ZRqkLCZkvILs5QAEcprJaZYN18gi/Xmt8fcXqPumgLls9PeiwofXncuDOYyuBwENhVvxeUeRrD+o0957yCEl1OyBTL/foHKiaTuabonOYF2Fq1vqgnwCR+6SujdPD0c6dIDlw/UllNaS2SstyEyZR0aLoFzOgrGplO7O8XDoTLZia2AJ9C8+JQ87ftNEwssrplfPyWHGNTVd8wSrFLquyvzcoTPookMUORC3j7Vk8kFp9LBXZlBJGCzj5GVspVh4CzIHYszSseVEByRHhWu9/K16pjKa9cLx7O33aS7eQy2eEAL8l3/3iwzzRI4R3+/ZvXzO/vIV/c010R/hPxQKY62Y63lf0BJNjllM58p9OvB5soKEOPw6m3AVLDvForE8ulhQOUvbNFSXkc0gFhvDXO6zycxVxJuIr0EPGd1n5sEzbIaCYEvsa5RBOSXIEqWbSOWjuZ5KkGNi3I+YYCDo0lmY0BEqZVnULVE7Qgq0Tf0AGTeupm5rjsMcQYyMLYTTLJVlVYIdo8XCQBmDcx0pNayX3THpngqpe5wkiUqD8CjH7YZQa5SZcU5QM6UFBZmjYQyZYd6RmVm0MM2JeY7MFpJRRwNHh6DMonJbyLnmDv0Hjh+NUkd7GP09JCufd/x3DVDGceSv/bW/xp/5M3+G9VpIUc+fP+fJkycPT8Jazs/Pef78+ee+zzRNTNN0/Hqz2Tx4XZEx5CNZTx/Y0koga6XFnEopW9RTFSpLCcYtTqnqBcuTt7C2wrmGeeqZhp4QNVlVaAUpTCw6TQwzw14xjBPbvSnRs8aHzDgl5gjKahZNzdnZCqOe0vc7hqEXKWdtcKUzxBjJZJQ+BCVwkNAHjkHKXd394VIcUyBlT8wGlSASRGKbO3XM4KUcMZsZoy11JRm8dNE4FJbaKJzKzMOOft9z9eoFN1ev2e82R7E7V4la6zhOeO9xlaNpWparFdEHxt1QFumMUvJx2O/YOc3V65cYAuuFE6IiiXnYMw175nlCpUDWhVNS1xiTqSvxERp8ZD9paZtE2P51U+OUtMjVtaMyCgZRDg0S/6CSIoaEnzzJh2Pf/oE0CRwJkYfMGuBAClJWnKrjXLCQzJHwKO6kUDnhbSitSnaasIpjHVnE5GQxO7SwhyAdQxrIRoMz1M7gjKK1GrwnjtMhIn0wysVqoXxeWsZyyV3uByhZH0TLlGzg2WCrhtasCas1elwThmtmFbFB6tx9KIqTypZndHet0yAbSVWBtbqY8knHWM7inKx0RmvQHGwe7s76/mcSi98LUMjluoqs+WfWsbtndL8icPhaHUnU+VApKu+pyvfeIEADOUpnX0yBcehZdC05eeqqElTFOpyLpLolzKUsGO7m5pvCUxJs3E3PqEBlDZEjr+Bg9qmVPLsQCvKj7+b78f20EC+NFpRsvL0l9Bs+DQN2P+M2vcD0bU23WFJZhzMiEz9PMyoJZhyBVLJbkTfQx7Xkztn2IAZ2MMYrnTNKziuiZB4czDRTxha5hDhHMAodPdbVNJXmyZMLzt7/Iu2TLzMH+OZ/+CV6paQEMY0Mt9eM2w3jfifo473hoa1BeS1BYUGWcxL/+AMR/EhcLaUUHyONzTQVtLWmbRTrVU1TO1Zdzeg9izZy26ejAztkfI54lYgVUs6KiTAFpn5GleBAEhMlQpu2tGiWQOkoxY+UYObJo3OEJFyWlDN1cjgM2dUk7QjZU9fuwaww1uLq5jNBtj6Qde91Bh5I0M6UVu7KceCriGBhZhgnvJ/Z9QOz9/S7HX4ciR6yCxizlRZwciFrg8+ZKcI0j6QcaGuFCYkQIyGK+F1OIiCqjyPmrr35QPy+P3UPHaiHqfgDxif//QIU7z1/6k/9KXLO/JN/8k/+m97r7/ydv8Pf/tt/+7u+Lp43FYaEVhntaoxrcM0SWy+w3QW5qIkSEzpGbDYkbWhXj1CmJpgFHs0QLLZqabrHuOU5cR7YX33MNGy5ffkhPkb60UvfPIqkLGhDHxR+Trgh0uZI3WWUq1mdn4tT7dBS1Y0Q6HQl7OtJkVXEeNFe+Tyi8wOOxBubVg5SnppSJJqMrpV0luCZkZKLtVJ3b1rxi6hri1UOt3CEORLmwOvvfIuMYn32hJhhZTNhWTM8OycW3sbbb51xdnZKnXvSeE0IFU553nnrgsaAijPjOODDjLWCIpyfrFivFixqBaHn9tWnstjHyNT3xHkWVV6lmL0npZmYpDsjZYWhEE/vZdc5J3LwZCNZt/eR6BPzNOFDROcakgGfUD6An47qsDndKxYfigJlgzvAqe2ywdWO87MzQgh8+vFzwizkQZJ0XRCDMPKpZIKWtroUEyEndIpFRt1gjHiGzJNkhfMYiEoRFEAFyWJ1IBtFtaiwGbq6pbYPSbLHCV8yLDGkfDgs9GFBVQJlB+VQSZ47qiPbNYvH79I1gXHaoLZg+r4EUYopBPppIBc79uBFUXgq7Z46GXSSDS6WLp7oAykE0RWyokws53v/3B/c8gcL2QEhyUq6Wt5cyPKDQOC+Hw8PXFJzflM/5i7Iz2+8jwQKgooEn3j94jl105Bzpm6XLE4fYVQjVgBkRmMZ9huin0lxPgaqSulicVHUOwvymYpWRirkeMmIpX05FsLtAWESH6WH5OBcgqkcI9l70m5L1IpgpJuutvClr3wR8/Z7dD/5P2NPz3FjZri65MP/5/+D7cuPef3t30QFj4oeZ7KQ4rV0pyR/h3YGXVRmVaJQV4rthmTY98H5EOVi7CxzLxX12DAFdtsdl8NE4DfZ3nje/TGLcg2h78XbCwXRMw637DaXbK5f4ue7pNMYw2J9Ip1XIR4TCvkjhZ+SM77cVzEVzUSlqJ1itbCcnxoWjcboipw1k4+gMotWYw2ly00M1kLviSbI/R0heRi3A2kMmEr8nIgloD7aKmQqa6mtEzQ1i+YOKjP1njxmohFtGbKmXTR0dcfJyVKQTpVYLk8eoIu2qmkWC+66YErJRN1XNxci6oHfFoLMk5iTNAsUxWKrFMvakSvLqmtJOTOHC+YQ2PZv47qPuNnOvHx1Q7/bFel8iNkwe/iNOHDRGN5ZO8aY2IfMetnQOMtpDsRZMTFgtSIAJsk/XTqXcuHrHT7eDWj4TAT2PR7/XQKUQ3DywQcf8Au/8AtH9ATg2bNnvHz58sHPhxC4urri2bNnn/t+f/2v/3X+6l/9q8evN5sN77333vFrWbxLm5wSKXptLMZWaFujq5asK5JuMAlcymjtyMpimxPQjjnaokKpwIrbqdOKqm7JYcAYy/72UiD6LIGBOO2I43FICqJmChkdMnMQfx5T1biYyGiappEJmEU11caECRlTSj3pkGUq9ZmF+e5iDx8UlWtIWZUN5VCOyMcoX5MLSqLu7o0qejBFrl+T2d1uJPPK0pJtcqRxmpNVRwyWGGbWi5pVW6HiTJoGSBGVPIu2Zmgr2saRk0epiHNQOcOyq+kah1WZHGamvZRZpJ1bavGH7qUQDgGa5yBARD4oub5xlEVLhIsKalBQDo6iTLJJqILqHLIhCefv3U6lUEZRVRXWWVbrNU3XcPHkMfM0c3l5LW81zoJX5ETKsggdNkxtC7lUiSsz6pCdGGy2R2g6hEgM0g2kfYIgHTcxRIyGVEvmZtGfMZC7y1bK03/wsoyPkuMJsQ6BYJXWkAxJWyI1tlmj0immalF2j1YDWgkcCyIbXzD/4/se2gZT0QAREcNCACwlO1HgvvOLktM8Dlb5+nMWqYOgGUrKDPcfzgMOFp+dF/d/5v7PfeZ4EJwcAjx1LPVN40BKkaHfo5RhkaKgsE66PmIdmUdbMv67QORAxL9zGr8j8uZMUZ7l+H2tVFG4TcfgKpXM9LOnXITeUiaFgNKK5CcxgNOa5cmC5bMnPPra12gePyP3ie2rV9x+/BGeQH71CUw9eYasYwnQi/hgyMWSg8JZKh2y6pCly8agEP5AcZEr5yPCfDkBSc4xBZjHQL/xVK+vQXVcPL3ENh3Ji11FUhALZyeESTSc7pW2lNZUTUMMAescIWdSFORE5TskLOV4bHWliHAaA5XTtLWiqXUhr6qCcGScUyUgLWuFSiQv94QcYZJkL6SAipkYDcpo8ZdBl/UZKdMphTOWXNqS56IhlUKSjh0V5LcUUgV1mrqqsEajDLT1nd4NSGJknCtrdlmj7gcoKaFUIhIL8TQdfaEEsElHZWVNaf9VGqdkLhqXcDGhrGW9WrJaLri+3h7vDTlLeT7BVY40GrSqscbgbKJylrqy6NqSVARny7g/lG/uAur70+3IUPjt5uX3cPx/PEA5BCdf//rX+cVf/EUuLi4evP77f//v5+bmhl/5lV/h9/ye3wPAL/zCL5BS4vf9vt/3ue9Z1zX1Gw/2/qG0QNimLLS2WWHaFaY9QVdLqNZk05DNAmvFmybriowlKkvMijAFZh/Y9wN2Chg9cr5uWTQNT979GoSZxWLF7dVr+kkx77b0ww1JG3I2tHQkKjajZYzgGenalpOTFbpd0nSw6CqsUUxjjw6BGk9SgTqAQvx00Hc+CsA9MmdC6XQ0i9Xa8JM/8lNUteGT599hHHv22xtRYw0zSRmMgooGbSpqt8BqSxg9WFCVYrXsWNQNw9UVY7/jgxcvQRuWF49orOUr770l3IoUWVaW2mb8zUvizqKqmhAiK5eInSata0YXCR66ylJXjmePzqmswY0TwU9sp4FQMg/dtGDMkXiaZ+E5+FLDFRKaEpfk+xtMiqgwEYNiYsQtG4wzLLoOPScuX3vCHMnjTI4eZzMkjVYOqa6U5r9yT61zNE3N4ydPOL+44L2vfIH12QnnF2fc3tyy2/dcvnrNdreFXOriJVOPRLRRrJYrqsqyWLb0+57ddk9dNVjrMGZNjJEXL56T8kw/DCyMoelqNA0aS97cElJpz1EKFQoTr70b4+Yg53C4Dw/itrJRUoo+ysl3dE1Gk4xhTAtuM9TVU0zMzNU3CDYwpx2ZiHGCWJlQyo1AVdVSukKIxbMfipmkRzcN2jSo0KNCj9MJYxRYW87ieKYPPyo4ck7IFDNoDIK+PNRMefj18WoflED5zOefee0z7yB36iCalXJmnmdevXhBtyimgo1wUlzdoIwlhCAaSqX0ldQhmNL33rVoT0g/uVxeMQpUSoKDgzjcfYPAdG+jBiAY8I4QKubomFSFMTBXFqcyXkXq04aTZ6c8fu9duqdfIMWa03d2tOsVn37z17FnK26/8222zz+GfkueZ/IwQUiUCqxwJbKUxLTKh1YCNBCK0rYzEqvqhJR7UiYlQ8oKrSsUCj8pxm1g+8oz77/D7UeX2GGmajum69fEsccXXRCcAJxZl6SiHNoY1ien1HVDZR37/ZZ+tyUnkR2I/i7hMFqE+OZoCF7jrKatYLW0LFpJEnNOhDmgsqKpHFpNR55YIhLHINopyUNWxKhROpFNII3y+IwWz5r1UkQTYwroLKhptzjBVg37vicEzzQNhCheXspqdGXY9yN+jNTnJ1RNxaKpaa17MHetsVRVVUood6PzUJ5MJdm01pQARdZAsjhtZ0Qb6jgCtSpda5qUEttdL+9B5rSt+dEvvc9+N3F1s2eeJTCfw8wMzCozRI1qwOuGZt1RNxbnFHa5AF9hJKOC6Itq8N1alEpAnmIpHh4bPn5QiuwPEKDsdju+8Y1vHL/+rd/6LX7t136N8/Nz3nrrLf7kn/yT/Oqv/ir/+l//a2KMR17J+fk5VVXxYz/2Y/yhP/SH+It/8S/yT//pP8V7z8/93M/xp//0n/6BOnjkOCwUpdilrCAHukKZiqwdaAfWoWyFsjWoChDnSYnMBbb2fsbPkilXOpJDRXagkscnQ8iWpBrmONBPoKyI3FRUaNWQqJgjXN+O7MdIP8ajYM/puqWuDM4qUAZtElpnzD2J8DeDzc+TPQe5zNVyzWLR4INnHHsaV+OnkXnoS2lG4OMYSmlBZwKBulHUdSMlCGtLjRxxc82Qb29l424bMVskMcyaoBXzPEuLXSW24TEmlJ+oVEIZgQsbY6i0huBFkTN4UvQEP3EgDR7kx++e2R1x7yChrO6R+Q6HVoq6LO5KST3WWY1NBlsgWQ5zXSuwGhULooJkRa6qj9yAumlYLpc8e/aMi0cXvPXOO6xOVpycrHHWcXK6Zuj7Qk05RE7yt5tadG+W65amrVifLOg7R9seFEwdYPE+crvdCCJUIswYEjpmdJJrNyhsyBiVxMH5Dd0AcyglHRCU+2Pk+P8DRlRaa5Xw6rOSEkpMFcku0e4E05xi2x5vX5HDLPVlJYFQLqiGBGNgrIKoiFF2K/HekO4QlQIqzqXbK5OOZZdcSh+HGXovYFBypqp0Z+jD9WXDpOGN7fr7Oj43aPmcIOcO6bi7e8F7/DQx7HeAwrjiiG0s1tXknAmjOSJnh0Dn7tIO6JEutQF9HLzikHyvs+MB9PPGuaHFl0hLJwpa2oypanKOUgoNgTCN0qqfI8YZXNeyfPyE02HH0y9/DWs0tnKEq1fEfs/8+oqMB+2lrHlAaQrH53ApCUAJylK86QTpugNTSv+YQWFIpiIZT9SGySdCHuhvb4l+Rqcg44IMzmIXS1zX4dr26MIr1ywkWescddsSUsDHQPSGGOV8c5IOEaUtqurIcyb6Q5ijsMZQWc3BNkRjxI6h3OI7TkeR/C/7hkKSIa3ksaUoaIjVGq0VlbOFeyTS+1oJ91AAxMJHkapoUVOWwliMnikkxmnAqEhls3i15eNQkYREaQ5pwWFmyxzQ94Zu4lhwE4KKyPhTuIsFDTk8T+89wQeur69F5iJntvup+FJlUIqqcuSsiQUFDUUf5br3KGNQVhNai80alCWrjFfS/qxVLFYfqlxDsengzvbhwbb1/60Szy//8i/zB/7AHzh+fSi9/Lk/9+f4W3/rb/Ev/+W/BOB3/+7f/eD3fvEXf5Gf/umfBuCf/bN/xs/93M/xB//gHzwKtf3Df/gPf7ArAA5W0tqIX4GUc2qwLdgOXAOmlgmuHUkZycwxRdY+EZPH+4Fxf8vU94z9ntu6orIGncWV2KrEOEzsfMP1XvPp5UzdOarGUasTnO3AdfTDwHc+/BbTODEMvUwYpXj/3QvOzhb8zh95n6auhCRrFFqJo/JBLRHyg4Xs8wMUxfn6nEePznj3nfcIMbDf7bi5vub1ixe8fPGC25sbhiEy9pH91bV0YljN+cUZp6fnKG0FMnYOU1VkrRj6gQ8+fo5SMjGtVlitcUoLOlUV4avaUjnLctFAitQx0mqFqqxkkDFz+fwFIQbGacQ6Q1U5uuWKuhKNB1mgim5GVeDXUieOJVtK6AO5AoDaWp6sumJBrqgbh7FGBKmSwjARFGQrYwFbfD1UREWp1z5++pRuseDx0yesT054/PgxT5884fz8nNXpmqquqGvDzdUlX/vqlzBEPv72N0u2L5CqtZZnz56wOlnw6PGCblFx9mhRCJWBnB05GW53mX7w4BzzNDFPMh52+4FlY7HOsKwqYfuHjEqRME/YKjx43s4qcPqY9d+VP0rWIkARoDGplDsR3w6vQWVLzAuyeoqyC5ZPf4S6WRL314Rhix9u0UZR11rKhsV0L2eFqw06ieJwTjMpjDKWVMaEHj1vcXicSWhXlv2UsUX5UisphnJ0ppaxX1cO5xS1A+dkq/jGbyp2b4zzNzVQ7ngon1ca+T5XwkOWh+hpjMOe188/pVsPTCHTrU6pmo5uCWGu8f1WShNFV+nOYSWj1IEMK0Ga7L/5aF54lxHnB9f0ZseWQeOUparqotPiMLXDnjyGqWfeb9hcXcMHv8XZiw9RjaM+/yK6dpy88wWa01Mu3nuXy+98wPWnH/Hqm7/B9tVLPviV/8h8c4uZb7ApUx+ijazFoE/iUBKZMQonJemMUxqFKSZw4vWjk2LMFVq3sHgsMv9+Yr+9IfR7Tq5vWEwDrYOcNddTxCwXrL/8FU7fe5/TZ+/gmg/uPQdBnqq6EcuBpsG2Hbv9wDR5sEXzw/dgGlg8Jm53jP0NU9D4oKldzaIxNCS0FiXxKQXml7PI/6eIGJAmWiuu1M5SyPWikSX2J6LI3C5r2rbi9LQTHavRYmyLsS3T7InDzG6/EffwJOWV2jUoE0B7SQrnxKs8snGaYXScxsc840fvJRjS4hGSeNtYJaF9KHicO/7kvXJY4Z+ZUgY/jq0ci5px5tXlFdvtjq9//etM84z3AZ8dc6q42e4IOfHodE3tNGke8ePI5fMd+23gt2Kg0pL8GfuM5bImRE1KhjE02DTTpEinBGWz1opfUi46L9kLT6hogknC84NFKN93gPLTP/3Tv+0f+15O5Pz8/AcWZfvcvwmFiyEmcmKfZUmqAl2JV4Op0Ua6eTJaot6cmYNIWc9+Zp4nab2cJqZhZB4HuenRF86FmM3dXF+z70cSBpQDXZGyJiYZbGiLtg2jH3jx6obZB2KMtI0VAaHkyKoi4UlITTXdW3DvEj91FL8q33hw0VO/Z9hZqlZY4E1Vs16uIUj2sGiXDP2OMM9M+33RM2lomwVt06G0EZ0OrcSPo6kIUdq1yQIFGwQNUeXEwuxBK0wK5GBxh7p1PizQdyWUqchwq7pGWUOuHKppsN1CZMW1JkYxqoqHDqBMWfQPvhIPs1+lZAE/uHtqlEC/UYSJKo24ezqNbSoqWzPWNX4UnQCtNevzC87OzvjKV7/CarXi7OyMk5MTloslTddgraGuFGGx4Nmzp2xubliuloyDZx48JstC7WpN21lWpy1N63CNJQdNNIaxT8yTZ7+fGcaAsxVGGWp7WHLk2frZMzGCzlysllLmMgrnHqqLaq3QVgkhTqmjfosgwwcypmSPRmlUVhhKF4dJx+4ZQ4NRmdXZU5LLcPOEYaO5GW+Rmr4YW6Yihx6FKnPMupytqGsn5OacIEyoMHJSJZomS6CZpQzndMLpjCPKGDESdCnboo3F1RlnNVUlQlUpSkDz5vHbibcdXv9t150jyUVgggP6cYDDDwmoEB9FYXiePfM8YadRxnUh8Iqwmi4lkjdE/nTirjuGI2/ioSbMXXH+gYLxgyNCFj6X1hFrFXXbcPLsbdhtycqw7yfmTz7l/IOvk4ic2RbbrrDNGbZpqU4esUoZs1hi24711SXTMNO/esn+G9+EcSSNY3ENjiLVXvQqVCERiBLtHWJolIyprDxRZbx21IsTTr7845hgsL3m5sVH7K9eoysRDhSiO6zWLadPLnj65a+xfvIWTbdGm3tj/B6So7WRskeW8ae0Yc7S9q6MkzU8CzrnVCZEGGZFVqL3ZLWUPqS1OHOzC4zzoYMvSkhQWSprWXYVMUgL8jAG5ujJWZDa5WJB2wofQ5hZYoGQsvDoYo5UJpG1xN6HsoxEPEnWKasJKYJP9GOimaeHY1UVpEyLbURWSnyx9lCbzEktyfFBCPA4lO+G0XGF1ErE5GKM3N7cst3taJuaunKknLndz2yud8Qwo1Xm4vyEk67G3FwxEInRk1VCRSudYZWQjUPWdKuVjGelSPNIv4eQIuMUqGPGmUSlBUURAVLRRTkEKN9lCv9Xjx8KL56MtNQ5pVHaARIARN2SdYe2K7R1aFtJIJPAx0CIibG4O45DzziOjOPI0PcM+56+3zFPI34aSSEwT4PAduNASICpwNQoUxOTuI1mpK25bteML6758ONX7LY7xnGkW7Qo7fCpJqmOkHtCEu+ZAzeCN6TCjbnjYAioXq45ZzY3l+g0slifUFU1i+WK5qTiZHnC+ekFY9/z4vlz+t2WV/45RsHpyWEzPsH7iWmeSFqjKku36kBBbUXy2ORIow2ttSTEbnzyc6nvQrJBBJ+0aM/kQszy+U5J1VhLs1ygrQFrsKs11XJFUzbqft6XDCZIfTWrAuVqEqHUyO8OnRUOjTW2kOnEYwgf0VFIXsZqjHEsliecnD5iv90x9D3p9SsU8Pjtd3n77bf5Pf/z76VrW7quw5qSBRQV4crJGXzly19i7HvOHz3i8tU1fT9jcyQTqVtNt3KcP15iK4O2kLyQRYZxy/Z25NXrW2afaKsVtjJU2h4L/ZvplmHcE8MOb2D57AnOGkbvBPW7d1irMQWFUuqwr0RiFMO8Q2dPRmOjLaCTEAKtjhK8J401LTbXnD77Enpa0Y4fsXlluH31YcnwMyEKeuJ9JkWYSwh0EJA6WzXMc2CeA3ke0HPP4yZyssw0bRTzznFDrSKNCtQqYonimaUVtCu0rdB1xFqHdTU+KiZfAszvd/5/H9mZ/KhoBRlj7vFQIHlPyoppjuhpxg4DWltySlSVKyU6CzqSmGXOHtVGkWgQeEgpyULgL0GKbJKKu1F9YPzc/5UAeULpCWM8VQWLkwXPvvojDJdX3GbH9evnTB9/SvfsPzBcP6dbrVHnz3DtKbppqZolp2ePOMmRR1/9cabdFlOvuPnOB/zWGPHX14RXL8B7iBL8HnxjOM7eQ7lHUFSrFE5B1DNBJUZbU5895a2f+l9IzZov1Gs++o3/zKsPv0n+zjdJ2xvccIt2ikcna55+6T2+/Ht+L4/e/grdySPMG51qFCVSYwxN0+JshdIGV00MWUsHEsLXSPOMJtNY8AE2gyKpDmtrmmrGx8DVbuJ2n3hxPbMbPLF4lEl3Y0PTOB6f1cw+stnNzPOeeRaPJmMMZ6fndG0NjMIlyokUi6lsmiFH1p1BG7mNIcIwSeeRjglnHMYZRi9ebplE1Y0PLlmVAEWbIrWfFVOEj64yqyrRnUasjpj7pPW7oXP84tBJFrKIQ7548YLdfsc7z57gnMU5x7c/esGHHz0nhAGtI++984S3z07oPghskmf0Ex5LCJZq1eJWHVkZfNKcPXqKthrVLthtN1zPiTQO5DHS6kSlM+vaUBlN5yxGZaw+0BaKJtkPcPxQBCjAkcmcC5cEHxjGEU2FrWZMUlhVHX0pMqZEr/J7cxKhs5A0k0/sh4ndvmceh6KQl7FFbTNFg0ERUVin0Cox9hvmac9+fyuOu8OA0YrHjy948uQRWmt+1+/6H3j77ad0yzXaGHzs8TERjmZih6u5C1DuZ2Cp1IzLK+QwkWbFuIkE62Aei6ulQfuAU5m2tqjcUr37Fs5ZLi7OZLE1GpXE3XV9cibkNFPRuIr+4hZixJBxxlAZQyiIUxWrkk0XMyxr8UkgYVc5jLM4c5DwzxhradtOEA+j0dkQ50gsJN5ju2VORB8JUxAya5QSPkqTzV2JJ+dM8AmI5KzEUykGUUxVltMKpgC7KbNuGp5cXJAvngjz/zd/g3n2nJw/Yn12wWJ1Su0qnHXStQE4rYQXRMBpzWK55Ozigne+8D4+am53E8pIGatpKrquZrHqUEYzh8Rm33P1eserFzdsNj27/Y6YErOdMNlgo8U1FfWixkdPyEEM1ZTG1mJJEP34Bv2yDImywXHI+lWRb6foqiiprB/9WHIRTivfRwkMm1BovaA2nnefvsWtmrn+aM3VfmS7GWVMBnkeKWV8CrJxVJbaWU5Pz+iHmWGcycnjxw1uuqWdKk7qET/dsr3+kEwkqojrWhpn0VqQPWMySjs0M0QL2TFPGj/KBvDfctwP7j8buJTOKuuoqgrnpPXXWndEpdAabSuquhYuRIiM/Y7opestFySJQ0eVuuOJHbrwDsqz+iBfXso+h86d+5mwlOjeOM/SHaV0BBvTJ7xXAABOsklEQVRJdUatatr33sZeXGAfPWX+z/8b/QcT46sX7NLMi9Uv0Tx6i+WPZ+zyFLt+jFEShJnlkqqq+NL/+FMMX/gSp4sTdq9fcfXBt9jdXrPbXJPniRw8aexJ3kO/F1QyisR5Ch6J2jVzkg6bOWVq43AXF7iTJ9jzt6iWFY/ff8on/9cd+3lgmj3aQbfuWJ4sWa7XVHXDnWvQvaN0xsUgqspKK+qqkrKsbYlZkW1HDJ6p3zI1itFF5mHiZuf5+nd6rreRp+eOySs+eun58MXE9WZgnGZyvus28zHST/DiKuN9pB8m+sETY8Jai3NiNCmICcxzYhx9sTJJLFpD7SxdXYRBOyPSE2nCOktTtxJ8oBivRrwXdXPvHzKsrLO0bUOKYgkwjhCGzPMbT1jAF86KFYWRVmvhwnAPZi/lQi3z3Pc9Q9+jlKKuay4ePTomNSebPcuuYtsPjFm4Y4RJUHyVCFoTrYPFgvr0hMXFCbe3e/ph5FNncJVDGcOyqTn54heYhoFpGAjTSIyBPgaGFNnOHk3CkaUEpKQd+Y1w9Hs6figClLu6b9FoiAliYJomtJpI84zFgDlIWZXa8QF6VYcapNhuzyExTDP9MDKPI01lhItRzMaSE36DqIYKtDtNe1LKTLMvm4e0pz26OGexWNAtOr72ta/x5MljmnZJSoEQpZ8/lUXqoKdxmLj369RKCUXq/lqW40z0mTiPWGNRfsYaizNOOptQ1M6gVU19uqKua84vzkTbYh6Fs5Mzy9Watm7RMeOMZn+6ghSx+SADlvFBINIqG0FTSrYe0SLUFpJs3K4WmWZd6qTGUNeNBChKobL4XGSXyfou8s9JTMCGccQXu3hrDcq6BwZbOWVp1c2yEczTTIgB52ochpVTWBLzFFhUjrOTE5rlCcY1vLi8Zt/3LE/OWK5Oabslzhhs8Y5QORXOTQYCViu6ruPk9JSnb7/N5fUO8+JKWjK1pqotdetoFw0xi7z6rg+8eLXjxatbtts9MQ0oElHP6KhRk6FbdSinBP0hFHnsIsFuRC7bvLFpHfRWYimDHTZHybwOx0GT446umQsxWL5S0hpKRlctdY48ffSIJu74aLmkDxDxYm4Wk6jgRuFQaK1RVuGsYbVagx7I2uKzJ8x7zLilmmtWeWL0r9ltfwsQvx+rz6l1h1UajcUpg1IO8kxKlhQsedSEPh9bYb+XOX+YI9/rcegKslY6J6qqwlpL0zRlvGqRJ6gbkb+3tvC49sTgONpSHKbo3Y1+oE585+Ei/rkcuSl353zkNfI5XJp8F6Aok0gVsKhonj2mSobqrczly09Izz9hur5iGDZcVpb66SvCo0c08W2WiwXatihlcU2Dalu6H/mdhH3P6eqcm5ef8uHjx7x68THzi0/wm1viMJBvrmAcUSGhvEenjIoi5Z+cFU5ThBAzKSmW2mLXJ7SPn7B8+32WZy3z9jHb/+2XmZ9/wuy9cDOWLe2yo10usVXN5wYoFCQrxKMnjXOVyBY0jqwtploS/MSuygw2smfi5SAIybefD9zsEwnHNMM3Pw68uvZsdqOUpnNAG4PWCO9wzkxTFF+bwtOQDiuNtVZKOkm0Y4LPTNOhBT9TuQXLrqY2YsXRtjXjFNn3gaZxLBdVGQuCigSf0CqJ5tC9x22Noa4r4QGlxOwzMUcuN4JKJCJKJ6xRgibfg08EiJN0+zC+vA+M41g4hBVnZ6dUlSOmwGLZ0rWOyoIhoqInR09SUUp2SpGcRTUN1WrJ8vSEy8tbttstWifZP87PaFdLnjy+YN8P7PuBzXbHME4M+z3Re/wkJeUqJ5yWUtXqB2S+/9AEKAfNB6U1MYzSlbN7iZp7EuBdx1SfkJUlKyOdPNoyR9Hg0BisqaiblsV6DSRcbZinAZUC5EgKE1Yb2qX43DRNwxxE32K3H5h9QPlieW4Mq/NzvvTFE5brJYtFx2q1IKXA9fUVKQZ2u15k05NCaSOGfoWt9l9j++eceXX1iv1OseoWOG3JY4/KSuB968BaYsn4gg/kPHNzW1QalXTJiCQ3xCALjlKWdrHAKGgqg1FZJlaZvCFLeWEImZgVPmuyT0Qf0E2NchXVspVNwEiHkNP2uKYf6u6xkLnQktEYIwRWYwwhhDulxpDJy8zBPz7ERL8fMFZjrSkdQpF+sycnsKpCJ02nNdqPDJsrlDFYwFaWOokrMwrGYSRqjUXjtAScWhfdBRVQKeKs4vRsze/40a+SlaZqWjavrojeY3RNToZ5ElvyD7/zmo8/ueTD77xCObBrx6OzhWQ//UQcItO1x6cdm+2EWTm6tiX3iZgznzx/jrWaOY00tLh7bcZaa+x9edYDUnBvTNwFItz9XAnGj7yXLGKGbaVZGMfj7oLGjLz7/nuE+jWvhoirpOV+HgYpnwXJPCubUXjGcYdzlpN6we1+IvotH37jPzHcnLM272LVyLOLBmfkvrZ1h7UVWQlRcffq06KlAk3b0S2W1N6wAEx+iKC8OQe+HxPCz3BTsiAW3s9AxvsZYzTTNBSCZi0OyDEwZNF72e93TONE27WFDGhQuRgy/leCI0G7uFfaScfxf0BZPmePRluDriqMM9jK0DU1XdvQtEvakye0p89Im1saZ6he/Bey3xGHl+xeDbz4957UnBGXz1guH9EuzmiWJ7im5dHFMxpXsf7yl2nfeYvVV75Mv9vQ7zf4zQ2x77n58NtMmw3XH35IHgf0fkcaBuK+5+bmmn6/x4+QDdSjZjHCZufhJFDlTB88wzxyc3PN7eUV2gd07YjOEI1IOqR7Sdj9Q9CKOzE+EljjRAwvR8iROE4ydrRlCHC5m9gMO4Zxw3eee15cOj5+dUuMiZvtWJLMSUozMbJat8KhchI8jL2YrVbGULcWVMfZ6Qld13J6foJzskW204R2jnH0jOPMatFxdtJh0owxitVqST8EbnYepRXjnPA+EmJCWUfdaoyKaPNQ6l66CUVoJaEIMTD5TL8bGerEnCKNKlpL2YjLzUFfpzRVCO8F0DBNA/t+T1W5krxIyfJ6u2O7H0kxi52Dgn6350ZDlRS3SnOtpX3chIzd7lEauraibc5Yr5aQ4fr1JVM/YBWcnax55/136MeZ2QeGST7ebmXO7DZb/DiyHwcucJ953t/L8UMRoBwIcMcMJUYyM3neo3Mmjh0xBhGC0k5IrFnIgykJO10Xp1NXVTRtQ04LUBE3WdI8kqLHzxLJdk1F09R0bcswTUyzWHcfyHOgMLZmfXLCkydPWSw72q4R9CUF/DQRY8B7gRQLjlDcxQVFSf+VZsucM/uhJ/qE05qkLcp4cYSNGV05QR+cI2st8GiOjINCHYIBbdFKixhUysfzMFaMoOrWiWuuysQQCtwvnIc4RUJWpKQxOmKURrsKXTlMVWOdpXIVRulC2pTaWiqeDqmoI5IPPfP6zqWUAzu9kM7uHSklMVtM5uiNk1JkGidylFZntBHTs+QJY8809kQlECpKxMj8PNPvdjhjcMqQnUNZC1aIgiK9Li3WlTOs1gvOL07ZbvcwR6ZhBDTBZ/r9xG7Tc/X6lpvrDdvtjsVFR9NWLE4anFPMKhJ0JvaQsixcTjucNQStyDGx2++xRoFLn+niubs3bxCm75L4EgTeX/0++6lGYj2hBCnqqiUuOlbrNYvtQFNX0v2ktcD6B8xRKUQBPOL9iGs6nKvRaiTGmevL51hG/LDCtZllZ3FG/hljUUrIgiknhn5flEIjOgfaSmOyxQEHvZD/luO7BzGZzEE0D3KOxKiJKUjgoSCaWMzixNhvGkf8PFPXFbkkQOp+8nCvIye/+XnKIltfgpJ85MOq4/cErn/j/LVBF4dlaw/30WG1pemWrB895eytd5ivXjAN3yHvBnKeCdOGzYvvMOhLdvoVi9UT2uUF3elj6sUK51ry+oSzi0c4ltRnp6znET9PhO0toe/pugX9zTXGVaRhgN2Webdj2mx4HRO72RN6LQT6ZAhRbD5CFNbKHDzjVHh8fU9XysE4R7aSHL6hMihPJucj6fgYoOQyWJEyes6JHH1RhLZCaE5Cxs0qsB9H+tGz7WdSzkxzIHhRws6F5FwVR3Fl5RnPM+iswYmkvdEy17tOpAOEpyTPvZsjOfX42RcBM4dFxOyauiGkWdSCU2L2kdkLEmldhbZCGnWu5v7EVKVMS5KWYkkmMkZFTNkDlKIo0coaEHPiCJ4ceClkyIlQTEyN0RhrBXFOkWEYmScP6dCCoBiniX2vmUJglzKjloBGxyQk8XGkbiqctSwXHTFEbq9v8fNEv++5OFmzXnQ0dU2Imd4HZh/RVU3fj4RkSLonRU3WP1io8UMRoKSc8CEcF2etZhH0mm/IscfHkWxbklvjmgW2WpDiHnSFtSuMtrhlTc6OcNLh/ZppHvGTIBzzIMznedxhtWbR1aIemBLGt7Qx8uitdzDG0bQrjHFU1QLrLHVdMY07prlnt5WHm4rZ3CECVspxUA7kuO/89hB2JnO12UKeuNnuqIxhWdc4LZyRanJC/DROSgNItG7rRjI066iqGmsriBEpLIRiECaCcT6DsRWurtBJOqVUCVDoMnNM5MmTJk9gpus6qrqW9shi6neoux+UNEUe3bO9vSJMA4Q9mkhVyiZGCzJybM1U5ljbB5j9zNXtjcCwRiT1jVJH5dh9v8VWFd3aofyecfOCF68/ZR8Slzc7YgLrBxZtx8ff+HW6pmXdLXn66BHnZ2fUbz2i6hq0SsTo6W+vuLm64sVHH6Jz4K2np5wtloTJs9u+4ubmmm984+v0/cjrV7cko2kbzdmFY3ne0i6kFa/RFco7LhYdfo6MY0A3Dm01oxZS8m67QxvpiDd2evC8xTlUmofLAPnMxqZL+UEdXufuRyk/bnPGkDA6gkr02TDpBrc8YbHuuTjr2A2BfgpYo4nBE6YBRcYZhcqB7eaKNnjqNsiGESPf/tb/yfZ6ze/66gm1WXJysRZlYjQ+iGmiTx4fAtMsHANrKkJWDPOMsknUNj+ntHW/W+fN0s6bKMlnTArvBXIH9lnKIiOfsgTk3svr/dCXQNmSjwGy3FOnFbWzsokcFJu/29y8F8DcF5W7O2dNyqUvJN+5vh6OarGkO7tgcfE202Zkuv4UOxp41UM9os4ST7/wZU7WLZ+sRvrrT9DzBhsTp5Oif/2aD7/962xmxd4rqvU5i5Mz/tf/y5/gnS+8z9OnZxhjwWoqs6BqlrA8gRRZPX1K9BNf2m/FUmIaGfc9/W5P+L/93xn+y69z82u/Ru4HlvUpy+oUq1qsclgNm5cv+eSbv8Hzjz5ic/mKt1YNZn1G9/g9urOnNO1S1pzvcmgt5ZWDf1HOmRRD+VxUa0kRGz1nnaJ994TdeWAcHZeveqYxMA2+iIZFlIm4GqxzWKtpFtKFZp28/8lqLX/TuGJ3UXgv1mCrjNFiCqqMJaUG7yfyLhbysObtp8+oK8s4J4Y5M3lPP83sh4m6aanrhveePaFtpDuwaxeFjPzwelMCUuL8JFPZxB/8nzSdg/M2U5syt4uHmyq8J4q+iQgBHuaFdJcd1t3N5pY4z0wvXqJvb1j5mVut2dcVH714jVKQJkH/p8oKOXqzIS8Mtes4Pz+nWyw4W4tc/1vPnrDZ7Pjok09ZLUSItHIV1lgWClpnaJtTYky8/fiCaRbEqe5W3/WZ/3bHD0WAclAFvV+fg1xgQU9OE0SFMg6dLCZbiepyQplKWiB1BVpjlcU6hastoXGk4JkaSwqeeXBSb2wqfJA2RIwjpkRTy8RbrM6wtqKqFxKpKpjn/mi8FkM4mmTpspMc/C4eQH8HUP7egvxg7c3gYyKnACnhjSgN1s6BrgXKT1FacJFWUVV8bVK06Bj5f7f3brGyZddd929e1qWqdu29z/3SN7evwbHjjySk1fARkNzyRREKhIcQ/BAQSpTgSFxCBEEiBl4SBYkHUARvmAcUIBIhIiKRTBw7CnScxLQ/Yzvpz91pu932OX2u+1aXtdacc3wPY66q2vuc091uJ919+lv/o629T9WqqrlGzTXXmGP8x3+YJFAkrWrMEQ2RlAlXvYiaduyVLGPer82qHZVw0eAiOJ9yVMauSYF596FEQc0p99+Qkn6F0KrIl3Ws+hGtdwbmuFHUMLorNfpjDBgHvnAkY0k2ZDlqoxU5sSW2DV2jN9oYhcPbN+nmR9Au2ZpMMDstk8pTFZb5rMJKR5Ili2bG/t4tDvZucrB/k5QsSRxl6fG2YP9Ad9iz+QFdG/Am4QqLHTkqL3gTkS4hBpzk3kiFVVJsUO0JAby1iHWQgu54Vo3I1rBG1SGPpW9O7rwBY2TDQdlM+Zi8Ic38FuNIxtEkR5O0lNAYR1142i7RBUvoNb5zSbGlV+js6IoO5zvIpL2mWbJcFrTtkhhVBDBFkCB0TSCESBu1X1IILQZL8loWGaKqg8qJr3p1Xub4tXDndXEPp+TYg5JtICvDSeaA0E+3POdMblyo761tB3rnOW7omNy5eZATvzfXprV6MdmOKwfnxPvYosSPJox3z7E8PePw6gxjSxb7h9jJPnbnFhIipqwpTp2lcol46JC2xeU0dGENpI6ujbCcq0BY6iCpeqo6WhZjNW2FU7FI47ZJKVBsjZEYkK7FLxv8fMHu177G6eWc7qtfJRpLab3eQGIkdR3dYs789i0OX3yR5WxGFwKMTuGmO0x2z1Fv7eJ8+bIVHccUSI/xdfpIq649pXc5ZTKhLA1dY1kWHYULudw20DcY9KXFect4otFdX2gnXm+0Z5b3upHr083GajWRzcUA6qh4fS5zPRZNRxcTLgqLZcuy7UiiY/RFQVkp2bqua0ajEYUvqOv6WJTTWKPtMfLGoyw841HiwumCwiaqMuFzmh3bR5TXatZ9X6eVzayutbKaV7pG9oKWReGxTo9dtq064UFlMJKGcDAp4r2jrivKssgpcR3ndGtC23ZarBACTdtSeL0vagoZjHMk5/C2oCz0R1xxcrl6RXhTOCgxh6QK5/MXrjdQ6wTjBOMS1idcEfG+xVtHkyJJnDbHkkr1F3yFrSbgJhhXQoqIJFVlTVm5UWm4LBYLZke6I08CxpU4W1BOdlQroh4T2oblYsZiseDo8IAus51hHY435Nfnre8q4pDWAlXrhW09sXUUhpgMXaft7hddw/Z0Gz+dMvIeby3h8IDYdSpTbT02T+hoLckXdM5rPyGBrmmQFBjVtTop1pGwBIEuGaIY2qAlp0VZ4i2MbIm1Bc7m3WWIKg0DiKj34Moi96bRSh6Jjno0ghQ5uLEkxZY2r1mSDG2nugXO2pXyag9feKant1Q0zloVArMGZwqIsCwi3llGoxLjwdgl07LD2cDR3iGLZcfNo32888wmE86cPkNtA7d9IIZD4uI2pfPMjm4yXx7xjetfYf/ogBdvXEOMB+M4fe4CVVWDaUA6aFt2RiXveuQ0OCG5yCy0NHsL5kHDqZOipjAGbwwmCsZCkwIhCHVR4VxJFbU79TwcaToqw0BWyXR3VHrJxl+ql9A33OvLRnunUBdWj8ehegadeG42hyzmnuv7LU2bmHpPMB1BEl0m2Rpb6LUQWn1cAoInJoP1I7wt6KwjCRzOFky3xqRo6RYtzbxlf/+Q5WJJG+ckiUSxWOtJ1QiD9qXxomq3x2/65k4eycvgbs7MMbqDsCLGH3vdOnQJG06MRumKTKgtaGczQtdlefG1So8SvZUbsDnelCJd1+oGJWoaWMm6fTSFlbhWP2Y/3qY6dYHL7/uzbJ19C+18RBcTz37pD3DfeBH31efZOjOl3h4xvfRO6suPcnT1q7SHB0jzdU6d3+K9Oxe4MWvYW3SYYot6POXBSxc4e3qb2B2RgidFR+FrrK9R/rQhFiVCSapH5Jws2hRE+FPecOldb+WZo31mz38N2wTKbkmY3+boRqRJR1z9fz7Hlad+j/mtm4SUKB56hK23vIVH3vtn2brwAEU5xtq78xH6m26M8VgrgD79A2RtDsFFh4gnScWkniIkzu92OQW8IKRI0+b12uh9ACsrXrMxDoujcDUWnY89Ybmvslq59wLWOCpvKX1F4Utu7R2xfzAjxUhROG7vH9GFRMIw3d7m4nRrNWfqutL1zzmsOb6eOecoioJkjW4OfUlVVZSFblhIjaoF50il5F5OvWNyciNTFgVVVXI0nwGGUV3hxjWT2mHHNTPAzBd0oWHZaTpzVFWqXN1ErCQ8idM7Ux66fIkWS4iRW/t7VKXngQvnmUxGbI0npCTs7R+wNRpRl56mjfTD0fuvobCOurDMg6V5FdnbN4WDklKi6zq6zqkst+3LK7WJm3WFCvh49ZZNJkZGY3HWqK6M0dJVJGmTKOs0xJF3vpI83vq8E0qIOFIymnsVEON10jstxcIIMQWaZqGlsKFb8y4AMMqH7eueeanceQ5vn3jM5ugGtm+RrqFIX1QrtrpKD0disiAB27RrLztEjHV0+Rz6yI6zGvlwXpsuYt0qsrFur217zwrrtDJCc6WsBLBM9uRjVGXGZLSPSwrdRkTIIMkQNuwSk2quGAGTjhdhWmcpq4LeaCbvclKnQl9K6dG8qwWc17RGKcKkNNho6CSpVHPskG5BszhkXnntd9O2OOD27as0zZzDxR5dt6QurUaRjCGGGQ0tzgaqyuC2x0zGJbvbI4RIMgGzEHwSFlHoRaX67qGGXMqc88qF19LgMkdPXFxX3qzO22qu26zviLCyQh81WeeX1fHWyiRtcNiRUkds54SuIYUlXiJbXrlVrtwCd6SibEkwKWmn1y5mpcyE5PkhQMxjlTz3xGiVwcGsYXvRsQyGJF4rtazHOkdlSw0hZ2MYC8SOdqELvVin/aiO4Xg04pshyR57i1yBs6YL3/19NNCiOj4i2iOmKPK6YVS8bkVwFfR82FS2lZXiar+xiPnGos+fOL+7ZYqMw7iK0ZlLiBSce8ceTdOyNIk0GhFL0WZ11lHUW5TeYM5AVx9iOksXE+Mk1MvIbptwxYSqnnDq9FnqesTB/gFdF5jPW7a3dplu7VLWNc773PlaowDa10kyHy5pdcpWzc5WjZsUNO0CKwtinCHzRCtLmhvXCNdvQAi4wjM6d57JhUtsnblEvbWLMQ5zFw7Kyhyi126/zgj9Ji1raqx4GTrTLfQXFbZwCFqFk1KiLEr9zo1oMyGra6F+/9oK0NsyR2XcyjFJ+fdq6iWdD6bU0t3xeESzVNn4w1mD95ZlExC0hYhGTPpeXB6fiwVs5tkd+6pNvtcgyq0xurkrCqcNHqNFxKo2lNHUu5HjAp7rv4Wi8FRlSQxx1RvHOocbjbDlnIChHlXsTCeY2UIjQHl9NzlgbTCUVcF4PCIuO0KILOYLYqc9qUS0wjKlRNM0ufWF9i7qo4OSd079BupVXLXAm8RBiSGwXCwwRlX+agyuzJUxvsAUI1xRU1ZjjC/BFRSupLBeu2FZAzYiJiKxw/hSe4Pk7mwnCT4iiaKYUBQTQhQlRSVd/lxZrnZTbbvk6HCP5fyIbrnUXXHqLzSNkuQt17EwnX7G3aW8exij3qlzHpysnIayHFGNppRG8CQW5B5DQTsqhzDHGUvhek0GmC21tLRwGs6b7kxxhcdXpbIpnQXRRda53C3TO0KMqpjoLJRKqLXG4ItckZRJV6HLbdNTIPZ6C0EbeamNI00b8jmQy73Nqn/JZiGt85bRtKILQXlHRYE1BfN5Q2gSbavKmy4IVeVxRUlptOfR6a2CtjAsZ9rbwxMw7ZzD/etARwgLbjeB2LZceeGPiKml3IJ6XHLm9EQdFGvYP7jNbNZRFiNGdcHW2SnjuuDsTo3EltQ11Mkwl45bi0AXRQuArcEW6IVrDIXT1EHtVQRrhCMErSpxnIgcWXVk9Mvn+BVvcqrC6g2wSk67di8jNgV8aui6PZr2Fns3XmB+dJt2f441jkuXH8WaxGjnErP5nFlUJVXpWuJ8QWwDba6uEPTGYb06tSYkLF2+dpSzdPXmEeVoyt7CUJqSoqzxdQBrqYtxrgrTm9Cy7Vi2S+azA+17YzwxbFbxyJ0375fBZtTxmDOz+T5yl9Th8TdZeQ1FUVCPRuqsG5vJszFn2I6TuDfD7n0IPIlsbEry2+Y7waaK7bGPNwXiRmw9/E5G5x9htHOB5eKQ2/tXmMeOw9jhRzW2HDGaXGJcjzm18wixm3N45rJ6WV77hgXx1OUWZVFx5vx52q7l6f/3S+zt7XH1yhUefugRHn7oLZw+e57xZAsjVXZO9GajyrIBUse4EPzYc363ZLxXcG1vhk37dN0NQmtoriXmz/0R4StfxYUOPx6x+853cfrt7+LMw+/C+oK8/duIPW2aPeUISr9jy45Sbye0UWMfATPGQjKr5uc+r9NVMco2zeq/udWFGG1psi4D10iKyjfoj6b9zGoKiAgSclqvSITtLZIIt27uMZ8nbtxWzRFjoapLdna2GY1qJpOxFiFYpz+5hN2eiAhresmTkpYZB9EqIJ8cySTVvsop4ITQN9dEBHvMQVGRxVFdk2KibRvattVIi3NU9QSzN6NJsLM9ZVRXXL12g/mioYt9oUje3BgYjUds726zuLHHsmnZv71H4T2Ls2cIIVAWSm04ms21AMWQHTGBqE5Mr36r6/i9L7eXwpvCQQkxMl/MEVFVWGxJicWHrC2CA1uCG2F8pU6KrzW3Xo7BesSVaHCr0lAza1G0FVluY9+lTcQKnBXd5fcOinUgSZv2LRcsF3O6PFE2V9tjUef8eEr9eO+VRz/+WMoaKknUey2LmqKoKcsah/ayCTiaiCopJpVGL7yjLjRvaIHUBd0lpNV1jRVHUdTgPeIdiRZr46rqqB+zlgYbytLm3busFl9tDY4u1CGQgjonkgKpUbKwNbmlu1V7qwZHpAuq6GrEUMraXlXhObs7ZX824+Coo2kCyyDMDlpil7AxquJh4UjeELy2J7dGVSeLZBiNPFY03dERWB7uYxFCaBnbQrk5RKzVduNFYXGFJUgiSqQolEjpvcO7TLwrfCZXJkwKVNYjTrC2hZjoolb9JAqs9dRFgRQl4jyGLGxk1MGrbIU3xy/NwgiVyWWr/VzYiKLIxv9pbtMt5xxe/SouNoxkSbs8ZDm/zf6NK8wO9+maiDEF3WzOeLLFhQfOMN4+xekLDzAvKpblAc0iIDHSdEGJnAYwWluQJBGTKtlqIyTt1Hvz1h7GWurRmFPbU05NpxSA9SXLboZFGI9KvZZcwlpNe6h0+Es75Zv45tRjjx/b7xJZWe/Oa01JmjbzDpxGaOly47WeRyKrm93mhqJvznacF3f38fT8s2Pjw2Bw2pqjdozOXaDodihOb9FJpJGI82Ocq5mOdyldiSOR0hhX2szPMqTc8qNwmWdRVoSkTeea+YwXn/8j0uEtjq58hQcffpTd06fZOXceX1bYqtbKu9gRmjlhecjy6jdobt5gcfM6y9khjErc9pTpmbMsZw1hfhsXWlzbsDUe40/tMj13kdGpc4grVxu9uzknG9Y5Yac754RsrsdmLZinadF+ne5bGaARqdyQ1RiHONmIxKy1g1ZOSd76p/xAWqXZLfVolKtpHE3T9l8Y2vbLM9kaa8TNexXMzBymfpz3WtZ1nCYTy/tUVI7GyZrDtBnY2Xx9b6IiN3mt65qUEtev36YoZ5R1zd7tQ2Ib2NneYVSX1PWY2WKpQnaLBdIeQYo4wEmii5FRXeu5LnZw3mqFq6ASD9lZb7tAG3STKxuXk7Xre+j/7yMozXyuJbyhw1cjMJ4yKPlS8HlXUmsfkLLGFLX2degdFFuoZ59Uqjym9TSIveGPXVp21UtCmyTpxHEGYoi0zZJmuaCZa846ZbLqydDymgS2Dhv3uJP8d+yFK+80JvJiWuOLmrIa4VKHTYGIpY2Gw0VLFyJtF6mKgslIKF3mRISo52DzxRhFw5++1i5uhWqV2hgwXbe6cDGZSGY1fWayyqLm5816fF0gdI2q8mY9g9Q2pNBp+sxaLYXOAmFd/tHdW6KQtd3L0rM9mRJSx+FsxmIZ6JaRw4MWCYlxBV7AFAYpIHotuzYxUXvlxZTOY8VSJMf+suVgtiBJZNkuKaY7FM5hTUSMUBZWHRSvejkxBYrCUnqLcVpxVI8LSts7KBGJjtJ48KJ9jQh0MQGOKFoxUNf5JlAUdN0BItrLImIpbYU3x/P03iQquxne7udD7taaozIi0Cxv0x3e4PbXfhfXzQgsaGYzFocH7F2/xtHRoXZJMRW3bhxx6sIlHnjLA0y2TyE8QGUtS+s4vLVPaJZI0hQPG80dk4gqIIumbCyWtmu5cXuPZRtogvDgpQuEy3B6OmJclCzmBxgi9Xikiq0uYayKcql6bX/zvzfuuFlt3uzNndfWS73PuiPxyQvMrKor1g5Km6+1sEoT6I7+uIPScyhORkBPjlOOrdwnx2kxeIz3WCeMzpYYItucVecDAQqMeEzymBW7ODHa3UbrVwSTuw33/X0BFV8zlnY+4/rXnmN2xXC9sITD65y/eAknb2e0NaWcbOnNuWsIR/t0+7dZPP8CzbXrLG9co5kdIKMxfnvK9PR5jNxi/uINXNfhu4ZqvEN16hTTsxcYnzqL+Er78cpxLt3GN0KfV+kF8dSGafW1bBKjew5G70hiWBGYQVPYmgAyx6+XbG/ZSLXZHPlNvUNE76jkz7IanSAl6nFNWVdaVpylF3qei809tDRq4jHY4w7K3c78RCTU9NG19cTZcFI2qsJYp9s3f4pSyb6juqZtW67fuKXtAqqa+dERqQvsTrc5f/4s48kWs/kSd+U6s0NHOnC69idVEe9CpK6V5Nu1DdYadXyy2m6IyqvqQkfTdRpRF3WSNsdEn9Z9FXhTOCgpaf23LJQs68sFMTps2VCmkmJssVFr9iVabFCtD0sJFIg4hIIkRrvnrrp8rjP8Qs49G3Bmfcn3nYr7PhYStVJnOZ/RLhek2CFynB20OVH7m0zvcEjqG7+t3W2zsXD2EKCNHV1oCR1Uoo0LravwRY0Xi4t2g+QqdEkICVWJbDpmnSoJOvScdrZ3cM5TjkaUozHlqAbvEOcIsSNGaJoGEajqkVbs+CL3GnGauklC17QIUNlCJeZTIoVI7Dqt8ugabNIGjBatxgmSeTACgnKHdKk4rjgZm4bl3jXsvGUrGdomsVwIGIstLdNTjrIyeFRC3orDWsEURnsOGUMVwQmUJhILaCcFyat2q4kBYxKndiYYGylHBpzQtXNwFl/arJ2jZN42dhRuQbAeYwNx2RCXC4Joa4Ct6US7fjpN2lRG8gKG9gaJSoQzBPAabvaFVgtswjnwXueAyQtwP4802tdHUwxt1yHtkrY9Is73ODq6RmoCaRmIAbytCWjH1MOjG/iRY350i8ILZ06dYxQDC29ZzmdUdclhq2WIYpTJ77xHBLou9vtQyqLAGhX8a5qWvb1DSMLsaMbDF89xantCnXVR2rYvsdXKr9FohOsCxiasabmDp3EP3M1ZeSUclc3oX69/ZLNglr7erUQD+6aNXdeqxkRsV/o9fYRk0yF5JZGdl3KyoP+Gc12HIaceHFCud6iS0yRmI7yI0c0YbDhfZrX+GJMoSs+lSxcx3RHzdz/K/NYV5revsPeNL9AdfIUqXmc0HuOLkhgC7fyIdu+Q5c19jl54keWNfW5e+RoRw5m3/Cmqi48yml7i6NohB1eu0Mz3SaZlemrK9Nw5pqfOMp7uansFY7IzpbP2OPIa2vczWtlXn/W5SaZzDrGaPuu/apG88RPlzTirqdOiKFZcoN7hsbYnmq/nWH8jdUbfLImuSfqkqrkmK5iY0y1o1Fc3nb1Dkx317JggfSonOyYbEZq7oZ+3yisUxLqsu7ORCuxtlt+oP6/V52T7AFy+fIHpdMIL3/gGbauVhlVZc/nBB9jZ3aWsRuzswGjcYasSOZpS2iOWizkHR0d4abm1t8e502cZ1RVnz57OPYw0whM6TcmXZYkvS1xR0GbeS1gs9Fzy96CK5Xd1z14WbwoHpZ/MSRImRpqmw5iOsgngNAxto3a9lCg4i1bJJKO/rUWSOiepX+g3iHC9YWPKOU2nD6c+l92HGTNvIsZA2y6zSFDu0ruKKG/uqlhP3v6ppBLGdjNul3Fy7Y2SiDHRBSWDaqNCp7nOlLB990k0XJly+DKKesehaQltoxUxzjJOQonR1Jd3OO8zB0VDlaAVBz2j3hhlpvcVIqovkbSXhgipyrulfHISE7HrtLRYOiVwbkSlBF021Omz9KvxphVSF4iHRxCgSvq9qtdlVMNg7Cmy4qkVcCmHVl0WRDKCS7nftY2UHkbO0aFaMeSuyuNRkQWzItFGQlIjW1fkeWLpYosh0bQdmERrtWw6NC3RqJ9bjkZY76mrEisJF1q94STVd9DMSQAC4tQ5M1Z7nqy/+LzLs2vn1pn13310DzSSZ7NUukgkxI52MYc2QZtyGaQSmjUtM6dpZiznR/itmvFkjIwnmLBka3srR4y8lm3mm4i1lhCz8qdu+0hOSEYyYT2yXDbsA13bMh3VWoW0PQJj6ELEGv221UHQ6y5KuKuDcTfH426OwEke172O3az06W9kGjEp8lx2qwhK/3bqnGizORH1pDfTOPdK59xrrC/1eA+7WgL6ayAv15vNBjfWfT3W5XPr58/KowEE5yzT6ZT29C6XL53lRrxFOGxp5jdI7R5HOyVpNMIZS2gb5gf7LG8fMb9+wNHXb7G4dcThcoYZjbmwe4Zy5xxFOdUKuv09QrtAbKAa19TTCdV4QlHV69RErmK656nnxXDF7+mPyzfenhB6TLSwf2m20+rGaO3KQekjXb0e08nQRZ/mwaDtFvrFGVmn8e3qcL08V+t6r5nTu5UqvKZ+xObcFe522ie1fvoU+Wo+9zcJ7ubc5vGbXHmU5/T29hTrDC9eM3RdInRLJuMxO7s71KNRbvFQ4UuPOIsvDGcPJswK4Vpasi+Ro8WCs5Jw1jIZ1/m89cuL2v9Cy7Wdy2ur3gNiyI1fjTaL9RhEjm+4XineFA6KcypTrSG3yOzoiKYJuHKHECzG3cJVS4pZoBpvUY63sAslw/rxDsYpKc04lcAXEsnISsMDdCL0YnB9+A9W+1mtSEhRy4pnh+zfvqUXawxodcV6Uelf319iebkEUFXEdGd9e58u2US/G7FGJfJVv0JTPpXzeG8YjcaEtqU8XABhVeM+KkuaTCI7mM3pFomWPcaLBsqa7W6HamuitfOlYzRWoaEYlCMiaGqtWzaEELVXT9tBjJRFVg/NF7n3FqTEMiZ4CK2nOdonhU6rm2LK5dpa1trGwHyp3Uqtc6pGmdEuW/a+cZOyHFHWYyaVOlDNYok4sJMSkxLxqMWkAiceU6soVTIOTERMINmE1OogORFtjBcskjoQx3hcAIHZ4gAKdXwak+hSy/6sZdmCT3o7sN2MzhiicUiKJALLLhAlsbtVUVWW3ekImyIyD9mGHYtZQ7PsMEUAl1i0hhQtTVdhY3tijmsExWanLrOkcsg85cVcyYDbky1qexF58DvpFoe0p19kcXSL+cEN2tu3WM7mlL6kxLBddVjX8OwzX+T82fP4yw9hREO5W9tjEi1b0xGusCzbSMISQ6SLQpdLzjEWK4HkBNKCUCaMLfNO0nLj1iEhCJPxGOsc7eEBziqJWXutFIQo2Jju2GStyyjXV8q9nJO74aWcgD5CUlVV/lu5MSmtb+4xKxXH0BGjqnRqqWe+oXC8DPrVlES/7DGbIf+XPLYf9bH4QOZZAJKwxlGPJpw6d55Hv/3dnDpfcf6hMQfXb9DNFpSmoQgR33XEwxmzF66wf2vB7RfnzGYdTZvYfughpufO8dD/9d1sn71Eu1gyv32TwxeeJbQH+ImjOLNNde4Uvq5xvsBuRvnvcTIime9nzGrtsKubdB8N0jdwztNzgUxmyQoOg/Ij+tQKgOSopUje+BlWzo+5Yzy9GGJ2qSRXEiErIURF7j6cK2VSRNd5s3as+ohJX9Uo0qtBH/9+e6di5eiazR1J1szYcKqk/7eKoKznXK+sXZaebbvFu//UO7U/z2KJ9yVFUecokqr/WiN459gqHW8fwyGeWrb4gxi4dXOPU6MJLkbqsZZKd52wmM852D9gujtlujXJfYss3pcY6zHWa+qn62jbjrRsiHYCJwjCrwRvCgdFQ1t21e03xoDQ0izniFis38O1HUXsA3JgS8H4gNgK4wVTFJiUJZXJF7gDk9X7ejl4las365ndO7g55Na1DV2j8tGx61ae+IoMt97y6pIrm9NrHf7UTfHJhe/E4ic5wtuLqrGOJuFtDscXq/JqZxJiRfVDnCU6R/TafyFEbXRorGM2X1BUlTZbLD0lYK3Xkt2iUE8550V10VbZfgkBYsRb7WlDzjfrYp4JnjmkjtF0TsyLfRDt7dNHkjRKkzip3pVE25r7vFaUlSOWyqURI+oxiNGXdhCdEK0aucuiNUXWe0+F0cbySZCYxd8y6dgav4qMGaPlzbq5k8zlEazxmrsOkWgMIZPvxArBJiUl58UEiRrVSCGXD0ZNiXVd7v+jzqkSle+8yUlsIMxzMz0lRxtSrrBYOyikhElL7cZcFFipcTIFOpIsWDZz7f5sPGCogWSEo8N9xmXNbDqj9trJWYMjuuu2Vq8dSYmQhJDlzQVd7KJR1blgAjYvUNpwMLFsOmbzJcum08hPTomUyeZdqFnteDdvxevpr87JyZ33y93fV/n6l4lgrEpacyRqzYPpHRQVsooxZs2SlNeCzTz7erf8x+GkvKpy6tWoN++5+X/9dWQM1nuKesTW7mlS2seYI8KyYyHqbKYkuXmm5JCmFhlIqa0g6jNnGJ8/z+T0LtV4RLdc0M1ndPND5eeUDj8Z4be21DmxbpWJuvdp9aq6J2yX07L3spFy+PT6tjma13M+Vsf163Qf6Qa1h9mwk+nn2IkonMnlv6wjIVr6vN5orsPfZjWWvmRZf2+83z2c65PE6f4v2Yz2mHUs7N7IqSxjVaJ+a4sYI3VZIn10RzRK7VZCkVAaYWoCOGFaenxnNBK6WDJ3jqKwJGvpuo5m2dB2LYh2u1etsJBlNGRllySim88YoXjl18Qm3hQOivOWYlRg20RwSVMrXcvtF5/TcLZ/nnI8ZXzqAlvbpxlv7VJvncaXE9omYssxxVaJ9WAietOShCsrrPNZRTWHtYyAhByqzMWgFu0KGRpmezdYzA5p50eQhbOs9LnEnCYh51Zl3XGnvylJXzNHz0nJKSIE5PjXlaIgoZdBV8JmQlg0HZPRFn5U4UcTfNMqt8QLtdXKk7KwWAqcMxzNWlJAVT8XLTev3aJtOqz1nL1wjqIYYUyRBasi1ubIRyeEtlMuQZ9GMZbYKQHWl54ELHOqKwa9SZukN+8gwrLTzqhNl+iF3aTPpaaYEy/ryW2Limr3PM5DKmC6M2ar8sQFdDGSoiFGR2G2aBto5kLrGjordMsFVhLnt2uqyiA7JYsQOGrbtYquBFKwhFjivWU6mSrZtjK0SZ2AEBpCEyjqggKrnUGtQSqnYle2XySFznbM28iL1/exIWKXS/XVopBaFSizAXAG41SPoaoLfLGx2xChvfksc25DbEEiRpTDI7HJc6PXblAiZ4wdYbmHIVGPhfHYcvb8ebanBbODOYuDJTFEJkVisWy58uIN4qJhvjfjkQdPc+7MFkdHR+wf7BOjqh8vl0vaLjFfBiXZZq6QMZYUAs5ZJGk43zcl3jliWbF/OGPZNIzrkumk4tKuw1mTCaceUF2F0liMna9O22QHqSc+9vPgpe7dL8fv2Hx8remQHaqwoCdm9voVTdNk1WgVWzvp1PQ733UlXjrmsJzE3W+2r94ZuRvufhNT5wMMxhnK0TY7/hGKesR46yyh28UW15nPjmhSZKd2YE6x9cBpyrMF04cKDrtAQ+LSuy4xPbPL9OwOJiT2X3iWo2sv0M32SB7YmbL14MPsPvwWfD3BunIzE3VXiORqv03tjsw7MTlVkzIBv7d9X12ZJDu8RZnVrJXMnfpjV+rWawdFsmBa7wz09TGaPurLjNepsTtGv+K15Jvyiiy7jr70EZ4Ycl8t4ZhkAne+qzorph/XXSx2zPFab2VW80/0MS3PtjjnSU6wpshFCIFcNIlzBSla6tiyIwt2Z9copSCOdpngsdFx7fpN9m/t8WC4hDGWm3t7HM30epYUqb2hmfUyGuqMbbYm6R3OV+eevEkcFIyo8JXLOcAUdRMdOpCIREvqLGGxR+MEK9oEzZcN3pS4FHH1FKHEGHUaxIBJPpdv6rTqAydJsui7RgDVeemUz9G1DaHTm7ZZedrasjvm+nByWDhmfgpkT1v/AqMcA0BTROhuRiiOzVnnLFJo/q/vI+GdXykYeq8N+8qyZFSVBAPSdTl/mpT05CyldyTv8iUhSFRCa7NYsJwvWMzmWd/FqqBWSnrBQ3agctrBGoxxtCF3Hk1xFQmSGIltmzVQIl3bEbpAiGHV2VhDvGnF/9EGecdJstY5/LgiSUcjARc7CKpz4jDatDAakhHaGJgtOqR2UDhMqQteqLTqfOkMLVZvkp2FoNERSYlF01FE8C7lCJpFMtemdA7xMPJetWiSLvo4q80GC6fS1GiZtIhWMomkTC2R1ZqXk3SrVGFEd7ByIp3Xzq7T+FnfjASbRQUldbrbXb2tIKJhXhOP6D+op0s5JxQFHMWWtu0ITUfTauRLYkBCk7/TflxKuEo5gthXWqmQmVY3aWX8WkjLGMlRaR1LCBoN2z86IqWO3fFIxfgStCGxbANlVSv35i4rWV/V0UdRNvFSwYqXi2T0zkTXdagI25rsmmIiOa1Q6FPHJ1M59yoz3hz3S1UZ/Uni3k4KgMXYEucnlNUpZGzZ2lkiMiaU+9gUqWqPRMFPIzGUhK6mbOYsQovfqki+4PDgFmkZOHzxBrPDfYKxFJMpxaRidPoco92zK2l7DTivxRlfcux3SWltOoDrx46X36bMEcx6givNFAs441aE0/zqbBGTP9Ou1uye8Nqv3dbmKEiCvkXBSTdjnb7qnVOzmrcickck+CXP/67OyfHI4ub82/zRSN+GpIXJRSSZG9j1oouiXLEUA/bokHR4SLGYUdiasuyYFBVTP6KbLVg2LTdv3QYMB7MZMUZGoxFlWay5TpJyQ8NNm0LKZO5o7Cukvh/Hm8JBsYj2hSlAvCU6kJjoGl3EMUtMDITZgllzk+V+TXV0jqKeMgot5XgHWxcYX0OaKOPUFaRoMCaB8av0jGZsBJJ2p7SiPQyW8yOa5ZzFbEbbKIu5JzuFLCp2eHhI13VUVYWIsFwu82Emh5JTVnmFqiz1AtlwUIrSU9TT1WuqcamtdJKhKBy+cIzGFTs722yNK0aFZzoZ4VJL2N2iXS5ZHh1oWiUm7TTsHFtVQQF0rYqbexLStswPDvFORXzG0y2KsqRtdJdZlkXuKaPOnjHg8w5m0S6JKeFDq/0uyOTWoznL+VxTYKkhpUjTdnot2WKlCByDOm7O6i52c2G3paM8vcXR7JCj2YJ02CBWcCMtCa1HBcnA0iUOuiXX9mfsFLtMxmPqLY8tIqHuiBaWxpBsSSoqTAwQojYaCxopKRxUZX7v3C4eZ9muBWzkzGiEt44UI50V5i5ixg4/cWgjgoRbgg2J2KoicVEWEITUJUy0EI3qJeTwaxJomw5fryu/RITZjT8kHbSrJmqrCIvkckuzsazlsLcjC4Y1PdnQqM6BCxwtbnN4MGf/1hHgGFUTyhSpTMBLwopQ+pqqGCuBPAoxp3W6mNNXIkxyCbaxEWPBesF5qCrtck1qaQI0LXzjast4VDCpz1J5S2ETodP04OXLD7K9c+qOm6q19lhU4o8TPbF1Nput7NxfW5u2P+l89M/3N4VXgtfSObnLp+ffGlnACMY6DCX1eJuqDGAusHN+xuLoAIiMx9pstHQ1hhrDiBt71zmY7XPz1pc5Wu5z+PTn6Q7mHH7lGovb12n8iK1LD7J7+QJn3/puTj/8dnw5ydL2OcfzEs6J7gUNNl/zqxTOygmExKbGTI565mM70O7UuQJuXYrsMs/Ir5RcexdjndLpI2FpVZ2zioRZpw66Ucc/xL7LtxZzk8et1XVuld7ox7zp0NzhYMMdc0s2UofWrMsIeo5OylGb/p6xmX5s2zY72nljnM+ny92527ZlsWhomo7FokHajt3D64wWN6lu3iSVY6pJydkzp3jLzjm+8pUX2JsdceXFa6QUKbxne3vKAw9eZjIZY632X/POrrqWH8to5fEvoqN9FR7Km8JBUdlsn7uM6s4Na7WyJRNJje15AC0xJJr5Hl1ooZwQQoMptQ+PG53GlCNsOQLnMLGfLHady8xOihUNI8YUWcwOaZaLlaT9xuA0L9pzL0BJRTm0fDI83EdlUkrKmIbVRXpyh1aPK80ZR4tzhZaupkjTLAmFlgd7V1B6TVckb3BeIwUprT3dqtBUVZvL+pwkbJY6b+cLjqzVyVmVq/xrRyK0LTFHiyAhMWCsdsAVtOQa6yidRqJahMKg9nCeaHvHrA+w5vI9q+Fba/ry7U1zJoxr8aWhkpqjozldGxjhsJVQbWl8IU0svi0wtSeaRBNbqkLwtYXaEERYLGOOIpWUVvAepDREoymnENTuReFxtiKiO5HSGry3qLQfUDmESEAbA4oIpc9CRZ0uHNbqPPVlSWw6Umi1X0jXc5tUFTMkaJaJYhxOTHKNQvQRuL5Vj27SVL/GiKyck+xJI0kF1fqoTOh0UasqSxh7ZkdCih0hzmjayGwe2T8ocD7ivWUyGVPXFV2ION/gxFAUYLODYm1fRmlWuzWtYmvUOfXrhboLgcVS+MaL+1gisVvq9RMD1WSXcjw9pj/Uz/3+971v8i9/838pB+Gl0kJ3i8K8XGTmlfBQjjtDrzYAfs931/fefGSd22CVttAeH+As5WQbW9b40RYglKWSTJ31qrdCycQJbmuMrZa0yx2aqqCr57imYGv3FLsXLnHmoQfYuXieydkHKEe7mFzymGzKUcJ7EylMJnYc0/vIc1tWKpLr8zvJy4sxkJLFGK9zv399CDkSq1Fms/Eeti/nlz7a0Udm1s7EnRU22dHT8HB2dvpKoJx4ya81m5IVG++5OhNZj1OSEJOm9bvcWLb/rV3gNdWaVo6Jtk7pnZM+etJHpHtbGdC2GtbincOi9lwulsSmZdQmluI4mpyh9RWhqinrmt1JzQMXz3Fqe4tbe/vEGLHGMB6PmW6NqapSxSo3qqZWjmX+9MRqKXpVeNM4KNhCJ2PeIWDBFS57sD1BNSIxaXO6EJHFEckWFM0RyYEf7VAKeNnGW5f7vKA7XavpBd2hGu1OS6LtlnRty+xwf+2gxI3d7wbxqixLYoxan78RnovZu+07nSaj5NGVC9FfNBsXozEwmtZYX6AhIwtJtTXm8xnTqiTlVE9ZVjin5Zx9HXvs1W8FRlUB3jKPQXuh5E7I0nXalbRtCV1LUVdUo1pTSzEQ2obYaiQk5p0MJqfAjFEeioeyLEkWPFpCbZwhWW1SGEJHNDoeXSxVX0DsWkBpc3Ibk7BuSVE7pBhzcHvB8iBQoRGduoTkDGnqKboCtyjpTETCkt2ypBxbusoQO+HgoKN0hi3ncE6oCgOlpTPC/GAGEklBGNUwchUpNnSxY8vByFoqokq31yVBhK5LRFSe2nuHLyxpGcGmXObsKepaycFNRxsDTReJnToo0qpezaJJVFtrB6VPvWmDOV0MY+xJmrn5nEgm4R7fpijXQudXL4QmSRiNPcaWHB5A22o1FrIkxTllJSRZsruzw9bWhPFoTNdFvC8oSFRGRZpSktWi1P+OST9ruVjgnaeuR6sbjrYn6Pjq15d0XcPRwR7ZneTsxctsnzl3J0ly9b3ffYlb632sz/der7nb/0/yRU6+/mQ05ZXgXmXNd3vu1ZYgv4JRbI7gmMuy+ltY1a5Xk5ISGOcwfe/EKC/LgBimk5pJioy2HV1zwGxU0B4tqOwpJb97z+nLl5iePcP0zCMU9ZamxyWTx9F0i7lHyammdo43wOv/3lxTDb3S78bZ9uk2kxVk83n16XXTaaRWn1unIGy/3V/5OsdTQJvfg5K4lVtmss6JyLonTq/mbHI1z0pPC3r9hLtEUGS1edANhKYTu64jduqghCzNEDu9t0h2QGKf2olp5bj0Dkqfcu3nnnZCF4x4FsZgkrBcLGgXSyYxsqBgf3qJ6C1NWVFNxpyajJhOxoQk3Lx1K6eHwDlPVVcr7aC+d9uK55MrlQRdl+K3MJffFA5KKi7Sbv/frEote4PkSXfnfsJgtUSHUI1JriA2E2wosE2DdfsYv8Ban1nk7vhiJWl100hRJ1TbNFrR0irZU8SvIpoiBSIeyL0K8gXqfZ0jKYnCJ2IdV8PshYnod8MiWFetzkDE0M7L9YWA1pqn0LCcX+fW9QNK74nNkhQ65jNPiiOiUdJUqjKpss9biRCqsMqXRms1pWG13fgSj+0sLkWMyR2Rk9DKmHX8Y212BJaNvmavTcTO07kpaTRGSnVARASpAkYEJ9rgzgE+l1nbPNmNL9ffnNnCu7dhraH0FnfxIuF0oCw9zlu8aJWRHRuci2xP2/w+MN5ySq4lEZ1wekfLLgvr8UXCTYS4bZAE3bku82rAV57Sj/AuMpFIKaJpK3qRPo+VxNkikAxIgGJhcY3JDfYEUwnOWDo8UkSKacdWKYxCWile9s7dNEI5mhyz58F8wrypV/N3vdBtVibkubKBfkfY94SR/OV0LiJ1YufixZzy04iVsw5b1yzLkltRyY3V+Xeye6qjvNBmBVlWTofNufq+JL93Vrzz+KKg8MWKh7QesX6/kzPN6v/7cZsvX21YbMSBq6ri8uXLvDTu5rjcbUG887iTfIhX5hR8s4vtS41Pn9Nc/vq4p556iueff/6b/JyTn3DcQbnj89cb7GMj2phMq/fplWr7FEjoFqTUEhaqTNotuvzdW6pbc4r66xTVH6nSdu8omLTiVuSgH1evXl2NyHvPqVOnWXPxjo99U2F4FbG74zj1AFw/F/NZSHak7cbj/UtOnO6Gre78njerhlYckD7ako/p9VmOnUVaRxF7R77HV/7oK+zv7efI0TrtmFJUyYIY8u91w8k+ctSncaSPnK5SX+v1+FgUTTTq2UZP8GNGpy9Q5dYN+yR+zzTaU8talsUWTVshZNXo8jRS5DEaQ9MTmDHaUy0ZLYQ6YdQ+eBRepQ6KkW/dVX/NcXBwwM7ODv/oH/0jqqp6+RcMGDBgwIABA153NE3Dz/3cz7G/v8/29vZLHvvq3JoBAwYMGDBgwIA/QdyXKZ4+6NM0zes8kgEDBgwYMGDAK0V/334lyZv7MsXzwgsv8NBDD73ewxgwYMCAAQMGvAp87Wtf48EHH3zJY+5LByWlxNNPP8273/1uvva1r71sHmvAt4aDgwMeeuihwdZ/whjs/NphsPVrg8HOrx3uF1uLCIeHh1y+fPmO3kQncV+meKy1PPDAAwBsb2+/ob+MNxMGW782GOz82mGw9WuDwc6vHe4HW+/s7Lyi4waS7IABAwYMGDDgDYfBQRkwYMCAAQMGvOFw3zooVVXxsY99bNBBeQ0w2Pq1wWDn1w6DrV8bDHZ+7fBmtPV9SZIdMGDAgAEDBry5cd9GUAYMGDBgwIABb14MDsqAAQMGDBgw4A2HwUEZMGDAgAEDBrzhMDgoAwYMGDBgwIA3HO5bB+UXfuEXeMtb3kJd1zz22GP87u/+7us9pPsa//Sf/lNtn73x823f9m2r55fLJR/96Ec5c+YMW1tb/NW/+ld58cUXX8cR3x/4rd/6Lf7SX/pLXL58GWMM//W//tdjz4sIP/MzP8OlS5cYjUY88cQTfPnLXz52zK1bt/jIRz7C9vY2u7u7/K2/9bc4Ojp6Dc/i/sDL2fpv/I2/cccc/9CHPnTsmMHWL4+f/dmf5c/8mT/DdDrl/Pnz/OW//Jd5+umnjx3zStaL559/nu/7vu9jPB5z/vx5fuqnfooQwmt5Km9ovBI7/8W/+BfvmNM/9mM/duyY+9nO96WD8p/+03/i7//9v8/HPvYx/vf//t+8733v44Mf/CDXrl17vYd2X+Pbv/3buXLlyurnt3/7t1fP/b2/9/f4b//tv/FLv/RLfPrTn+Yb3/gGP/ADP/A6jvb+wGw2433vex+/8Au/cNfnf/7nf55/9a/+Ff/23/5bPvOZzzCZTPjgBz/IcrlcHfORj3yEL37xi3ziE5/gV3/1V/mt3/otfvRHf/S1OoX7Bi9na4APfehDx+b4L/7iLx57frD1y+PTn/40H/3oR/md3/kdPvGJT9B1HR/4wAeYzWarY15uvYgx8n3f9320bcv/+l//i3//7/89H//4x/mZn/mZ1+OU3pB4JXYG+JEf+ZFjc/rnf/7nV8/d93aW+xDf8z3fIx/96EdX/48xyuXLl+Vnf/ZnX8dR3d/42Mc+Ju973/vu+tze3p4URSG/9Eu/tHrsD/7gDwSQJ5988jUa4f0PQH75l3959f+Ukly8eFH+xb/4F6vH9vb2pKoq+cVf/EUREfnSl74kgPze7/3e6phf+7VfE2OMfP3rX3/Nxn6/4aStRUR++Id/WL7/+7//nq8ZbP3qcO3aNQHk05/+tIi8svXiv//3/y7WWrl69erqmH/zb/6NbG9vS9M0r+0J3Cc4aWcRkb/wF/6C/J2/83fu+Zr73c73XQSlbVs++9nP8sQTT6wes9byxBNP8OSTT76OI7v/8eUvf5nLly/z1re+lY985CM8//zzAHz2s5+l67pjNv+2b/s2Hn744cHm3wKee+45rl69esyuOzs7PPbYYyu7Pvnkk+zu7vLd3/3dq2OeeOIJrLV85jOfec3HfL/jU5/6FOfPn+dd73oXP/7jP87NmzdXzw22fnXY398H4PTp08ArWy+efPJJ3vve93LhwoXVMR/84Ac5ODjgi1/84ms4+vsHJ+3c4z/8h//A2bNnec973sNP//RPM5/PV8/d73a+75oF3rhxgxjjMYMDXLhwgT/8wz98nUZ1/+Oxxx7j4x//OO9617u4cuUK/+yf/TP+/J//83zhC1/g6tWrlGXJ7u7usddcuHCBq1evvj4DfhOgt93d5nL/3NWrVzl//vyx5733nD59erD9N4kPfehD/MAP/ACPPvoozz77LP/4H/9jPvzhD/Pkk0/inBts/SqQUuLv/t2/y5/7c3+O97znPQCvaL24evXqXed9/9yA47ibnQH++l//6zzyyCNcvnyZz3/+8/zDf/gPefrpp/kv/+W/APe/ne87B2XAnww+/OEPr/7+ju/4Dh577DEeeeQR/vN//s+MRqPXcWQDBvzx4K/9tb+2+vu9730v3/Ed38Hb3vY2PvWpT/H+97//dRzZ/YuPfvSjfOELXzjGVxvwx4972XmTH/Xe976XS5cu8f73v59nn32Wt73tba/1MP/Ycd+leM6ePYtz7g5G+IsvvsjFixdfp1G9+bC7u8s73/lOnnnmGS5evEjbtuzt7R07ZrD5t4bedi81ly9evHgH+TuEwK1btwbbf4t461vfytmzZ3nmmWeAwdbfLH7iJ36CX/3VX+U3f/M3efDBB1ePv5L14uLFi3ed9/1zA9a4l53vhsceewzg2Jy+n+183zkoZVnyXd/1XfzGb/zG6rGUEr/xG7/B448//jqO7M2Fo6Mjnn32WS5dusR3fdd3URTFMZs//fTTPP/884PNvwU8+uijXLx48ZhdDw4O+MxnPrOy6+OPP87e3h6f/exnV8d88pOfJKW0WowGvDq88MIL3Lx5k0uXLgGDrV8pRISf+Imf4Jd/+Zf55Cc/yaOPPnrs+VeyXjz++OP8n//zf445hJ/4xCfY3t7m3e9+92tzIm9wvJyd74bPfe5zAMfm9H1t59ebpftq8B//43+Uqqrk4x//uHzpS1+SH/3RH5Xd3d1jTOUB3xx+8id/Uj71qU/Jc889J//zf/5PeeKJJ+Ts2bNy7do1ERH5sR/7MXn44Yflk5/8pPz+7/++PP744/L444+/zqN+4+Pw8FCeeuopeeqppwSQf/kv/6U89dRT8tWvflVERH7u535Odnd35Vd+5Vfk85//vHz/93+/PProo7JYLFbv8aEPfUj+9J/+0/KZz3xGfvu3f1ve8Y53yA/90A+9Xqf0hsVL2frw8FD+wT/4B/Lkk0/Kc889J//jf/wP+c7v/E55xzveIcvlcvUeg61fHj/+4z8uOzs78qlPfUquXLmy+pnP56tjXm69CCHIe97zHvnABz4gn/vc5+TXf/3X5dy5c/LTP/3Tr8cpvSHxcnZ+5pln5J//838uv//7vy/PPfec/Mqv/Iq89a1vle/93u9dvcf9buf70kEREfnX//pfy8MPPyxlWcr3fM/3yO/8zu+83kO6r/GDP/iDcunSJSnLUh544AH5wR/8QXnmmWdWzy8WC/nbf/tvy6lTp2Q8Hstf+St/Ra5cufI6jvj+wG/+5m8KcMfPD//wD4uIlhr/k3/yT+TChQtSVZW8//3vl6effvrYe9y8eVN+6Id+SLa2tmR7e1v+5t/8m3J4ePg6nM0bGy9l6/l8Lh/4wAfk3LlzUhSFPPLII/IjP/Ijd2xqBlu/PO5mY0D+3b/7d6tjXsl68ZWvfEU+/OEPy2g0krNnz8pP/uRPStd1r/HZvHHxcnZ+/vnn5Xu/93vl9OnTUlWVvP3tb5ef+qmfkv39/WPvcz/b2YiIvHbxmgEDBgwYMGDAgJfHfcdBGTBgwIABAwa8+TE4KAMGDBgwYMCANxwGB2XAgAEDBgwY8IbD4KAMGDBgwIABA95wGByUAQMGDBgwYMAbDoODMmDAgAEDBgx4w2FwUAYMGDBgwIABbzgMDsqAAQMGDBgw4A2HwUEZMGDAgAEDBrzhMDgoAwYMGDBgwIA3HAYHZcCAAQMGDBjwhsPgoAwYMGDAgAED3nD4/wCR/GQNDG+VcwAAAABJRU5ErkJggg=="},"metadata":{}},{"name":"stdout","text":"torch.Size([32])\n","output_type":"stream"}]},{"cell_type":"code","source":"import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"3x3 convolution with padding\"\n    return nn.Conv2d(\n        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n    )\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n    ):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n      \nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n    ):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\nclass Res2NetBottleneck(nn.Module):\n\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4\n    ):\n        super(Res2NetBottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.ModuleList([nn.Conv2d(planes//scales, planes//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn2 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.scales = scales\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        \n#         print(out.shape)\n#         print(\"Begin Forward\")\n        xs = torch.chunk(out, self.scales, 1)\n        ys = []\n        for s in range(self.scales):\n            if s == 0:\n                ys.append(xs[s])\n            elif s == 1:\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))\n            else:\n#                 print(xs[s].shape, ys[-1].shape)\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))\n        out = torch.cat(ys, 1)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out    \n\nclass SELayer(nn.Module):\n    def __init__(self, channel, reduction=16):\n        super(SELayer, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)        #全局平均池化，输入BCHW -> 输出 B*C*1*1\n        self.fc = nn.Sequential(\n            nn.Linear(channel, channel // reduction, bias=False), #可以看到channel得被reduction整除，否则可能出问题\n            nn.ReLU(inplace=True),\n            nn.Linear(channel // reduction, channel, bias=False),\n            nn.Sigmoid()\n        )\n \n    def forward(self, x):\n        b, c, _, _ = x.size()\n        y = self.avg_pool(x).view(b, c)   #得到B*C*1*1,然后转成B*C，才能送入到FC层中。\n        y = self.fc(y).view(b, c, 1, 1)   #得到B*C的向量，C个值就表示C个通道的权重。把B*C变为B*C*1*1是为了与四维的x运算。\n        return x * y.expand_as(x)         #先把B*C*1*1变成B*C*H*W大小，其中每个通道上的H*W个值都相等。*表示对应位置相乘。\n    \nclass SEBottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, reduction=4\n    ):\n        super(SEBottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.se = SELayer(planes * 4, reduction)\n        self.downsample = downsample\n        self.stride = stride\n        self.reduction = reduction\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n            \n        out = self.se(out)\n        \n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\nclass ColaBasicBlock(nn.Module):\n\n    expansion = 4\n    \n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4, reduction=4\n    ):\n        super(ColaBasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.reduction = reduction\n        self.mix = nn.ModuleList([nn.Conv2d(planes, planes//self.reduction, kernel_size=1, stride=1, bias=False), \n                                  nn.BatchNorm2d(planes//self.reduction),\n                                  nn.Conv2d(planes//self.reduction, planes, kernel_size=1, stride=1, bias=False),\n                                  nn.BatchNorm2d(planes),\n                                  nn.ReLU(inplace=True)])\n        self.conv2 = nn.ModuleList([nn.Conv2d(planes//scales, planes//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn2 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.scales = scales\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        for block in self.mix:\n            out = block(out)\n#         print(out.shape)\n#         print(\"Begin Forward\")\n        xs = torch.chunk(out, self.scales, 1)\n        ys = []\n        \n        for s in range(self.scales):\n            if s == 0:\n                ys.append(xs[s])\n            elif s == 1:\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))\n            else:\n#                 print(xs[s].shape, ys[-1].shape)\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))\n        out = torch.cat(ys, 1)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out    \n    \nclass ColaSELayer(nn.Module):\n    def __init__(self, channel, reduction=16, scales=4):\n        super(ColaSELayer, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)        #全局平均池化，输入BCHW -> 输出 B*C*1*1\n        self.conv = nn.ModuleList([nn.Conv2d(channel//scales, channel//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn = nn.ModuleList([nn.BatchNorm2d(channel//scales) for _ in range(scales-1)])\n        self.relu = nn.ReLU(inplace=True)\n        self.fc = nn.ModuleList([nn.Sequential(\n            nn.Linear(channel // scales, channel // reduction, bias=False), #可以看到channel得被reduction整除，否则可能出问题\n            nn.ReLU(inplace=True),\n            nn.Linear(channel // reduction, channel//scales, bias=False),\n            nn.Sigmoid()\n        ) for _ in range(scales)] )\n        self.scales = scales\n \n    def forward(self, x):\n        xs = torch.chunk(x, self.scales, 1)\n        ys = []\n        for s in range(self.scales):\n            b, c, _, _ = xs[s].size()\n            if s==0:\n                ys.append(self.fc[0](self.avg_pool(xs[0]).view(b,c)).view(b,c,1,1))\n            elif s==1:\n                ys.append(self.fc[1](self.avg_pool(self.relu(self.bn[0](self.conv[0](xs[1])))).view(b,c)).view(b,c,1,1))\n            else:\n                ys.append(self.fc[s](self.avg_pool(self.relu(self.bn[s-1](self.conv[s-1](xs[s]+ys[-1])))).view(b,c)).view(b,c,1,1))\n        \n        y = torch.cat(ys, 1)\n        return x * y.expand_as(x)         #先把B*C*1*1变成B*C*H*W大小，其中每个通道上的H*W个值都相等。*表示对应位置相乘。\n    \nclass ColaSEBottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, reduction=4\n    ):\n        super(ColaSEBottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.se = ColaSELayer(planes * 4, reduction)\n        self.downsample = downsample\n        self.stride = stride\n        self.reduction = reduction\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n            \n        out = self.se(out)\n        \n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n    \nclass ResNet(nn.Module):\n    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n        self.inplanes = 64\n        super(ResNet, self).__init__()\n        self.network_type = network_type\n        # different model config between ImageNet and CIFAR\n        if network_type == \"ImageNet\":\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n            )\n            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n            self.avgpool = nn.AvgPool2d(7)\n        else:\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n            )\n\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n        self.layer2 = self._make_layer(\n            block, 128, layers[1], stride=2, att_type=att_type\n        )\n        self.layer3 = self._make_layer(\n            block, 256, layers[2], stride=2, att_type=att_type\n        )\n        self.layer4 = self._make_layer(\n            block, 512, layers[3], stride=2, att_type=att_type\n        )\n\n        self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n        init.kaiming_normal_(self.fc.weight)\n        for key in self.state_dict():\n            if key.split(\".\")[-1] == \"weight\":\n                if \"conv\" in key:\n                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n                if \"bn\" in key:\n                    if \"SpatialGate\" in key:\n                        self.state_dict()[key][...] = 0\n                    else:\n                        self.state_dict()[key][...] = 1\n            elif key.split(\".\")[-1] == \"bias\":\n                self.state_dict()[key][...] = 0\n\n    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(\n                    self.inplanes,\n                    planes * block.expansion,\n                    kernel_size=1,\n                    stride=stride,\n                    bias=False,\n                ),\n                nn.BatchNorm2d(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(\n            block(\n                self.inplanes,\n                planes,\n                stride,\n                downsample,\n                use_triplet_attention=att_type == \"TripletAttention\",\n            )\n        )\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(\n                block(\n                    self.inplanes,\n                    planes,\n                    use_triplet_attention=att_type == \"TripletAttention\",\n                )\n            )\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        if self.network_type == \"ImageNet\":\n            x = self.maxpool(x)\n\n        x = self.layer1(x)\n#         print(\"Begin layer2\")\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        if self.network_type == \"ImageNet\":\n            x = self.avgpool(x)\n        else:\n            x = F.avg_pool2d(x, 4)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n        return x\n\n\ndef ResidualNet(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(SEBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n#         model = ResNet(BasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n#         model = ResNet(Bottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(BasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n        # model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Bottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n  \ndef Res2Net(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(Res2NetBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Res2NetBottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\ndef ColaNet(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n#         model = ResNet(ColaBasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n        model = ResNet(ColaSEBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n    elif depth == 34:\n        model = ResNet(ColaBasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(ColaBasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(ColaBasicBlock, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n\ndevice=\"cuda\"\nres2net=Res2Net(\"CIFAR100\",18,100,None).to(device)\nprint(res2net)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T11:05:34.685109Z","iopub.execute_input":"2023-05-21T11:05:34.685500Z","iopub.status.idle":"2023-05-21T11:05:38.180339Z","shell.execute_reply.started":"2023-05-21T11:05:34.685436Z","shell.execute_reply":"2023-05-21T11:05:38.179361Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"resnet_bottleneck = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_bottleneck)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:16:45.003248Z","iopub.execute_input":"2023-05-21T02:16:45.003595Z","iopub.status.idle":"2023-05-21T02:16:45.404928Z","shell.execute_reply.started":"2023-05-21T02:16:45.003563Z","shell.execute_reply":"2023-05-21T02:16:45.404000Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"resnet_basicblock = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_basicblock)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:43:13.742246Z","iopub.execute_input":"2023-05-21T02:43:13.742599Z","iopub.status.idle":"2023-05-21T02:43:13.974772Z","shell.execute_reply.started":"2023-05-21T02:43:13.742572Z","shell.execute_reply":"2023-05-21T02:43:13.973855Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (fc): Linear(in_features=512, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"resnet_se = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_se)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T08:00:34.603506Z","iopub.execute_input":"2023-05-21T08:00:34.604096Z","iopub.status.idle":"2023-05-21T08:00:34.979456Z","shell.execute_reply.started":"2023-05-21T08:00:34.604065Z","shell.execute_reply":"2023-05-21T08:00:34.978439Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): SEBottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): SELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (fc): Sequential(\n          (0): Linear(in_features=256, out_features=64, bias=False)\n          (1): ReLU(inplace=True)\n          (2): Linear(in_features=64, out_features=256, bias=False)\n          (3): Sigmoid()\n        )\n      )\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): SEBottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): SELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (fc): Sequential(\n          (0): Linear(in_features=256, out_features=64, bias=False)\n          (1): ReLU(inplace=True)\n          (2): Linear(in_features=64, out_features=256, bias=False)\n          (3): Sigmoid()\n        )\n      )\n    )\n  )\n  (layer2): Sequential(\n    (0): SEBottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): SELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (fc): Sequential(\n          (0): Linear(in_features=512, out_features=128, bias=False)\n          (1): ReLU(inplace=True)\n          (2): Linear(in_features=128, out_features=512, bias=False)\n          (3): Sigmoid()\n        )\n      )\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): SEBottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): SELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (fc): Sequential(\n          (0): Linear(in_features=512, out_features=128, bias=False)\n          (1): ReLU(inplace=True)\n          (2): Linear(in_features=128, out_features=512, bias=False)\n          (3): Sigmoid()\n        )\n      )\n    )\n  )\n  (layer3): Sequential(\n    (0): SEBottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): SELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (fc): Sequential(\n          (0): Linear(in_features=1024, out_features=256, bias=False)\n          (1): ReLU(inplace=True)\n          (2): Linear(in_features=256, out_features=1024, bias=False)\n          (3): Sigmoid()\n        )\n      )\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): SEBottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): SELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (fc): Sequential(\n          (0): Linear(in_features=1024, out_features=256, bias=False)\n          (1): ReLU(inplace=True)\n          (2): Linear(in_features=256, out_features=1024, bias=False)\n          (3): Sigmoid()\n        )\n      )\n    )\n  )\n  (layer4): Sequential(\n    (0): SEBottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): SELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (fc): Sequential(\n          (0): Linear(in_features=2048, out_features=512, bias=False)\n          (1): ReLU(inplace=True)\n          (2): Linear(in_features=512, out_features=2048, bias=False)\n          (3): Sigmoid()\n        )\n      )\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): SEBottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): SELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (fc): Sequential(\n          (0): Linear(in_features=2048, out_features=512, bias=False)\n          (1): ReLU(inplace=True)\n          (2): Linear(in_features=512, out_features=2048, bias=False)\n          (3): Sigmoid()\n        )\n      )\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"cola_net = ColaNet(\"CIFAR100\",18,100,None).to(device)\nprint(cola_net)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T11:05:45.232171Z","iopub.execute_input":"2023-05-21T11:05:45.232538Z","iopub.status.idle":"2023-05-21T11:05:46.249403Z","shell.execute_reply.started":"2023-05-21T11:05:45.232501Z","shell.execute_reply":"2023-05-21T11:05:46.248427Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): ColaSEBottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): ColaSELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (conv): ModuleList(\n          (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        )\n        (bn): ModuleList(\n          (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        )\n        (relu): ReLU(inplace=True)\n        (fc): ModuleList(\n          (0-3): 4 x Sequential(\n            (0): Linear(in_features=64, out_features=64, bias=False)\n            (1): ReLU(inplace=True)\n            (2): Linear(in_features=64, out_features=64, bias=False)\n            (3): Sigmoid()\n          )\n        )\n      )\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaSEBottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): ColaSELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (conv): ModuleList(\n          (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        )\n        (bn): ModuleList(\n          (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        )\n        (relu): ReLU(inplace=True)\n        (fc): ModuleList(\n          (0-3): 4 x Sequential(\n            (0): Linear(in_features=64, out_features=64, bias=False)\n            (1): ReLU(inplace=True)\n            (2): Linear(in_features=64, out_features=64, bias=False)\n            (3): Sigmoid()\n          )\n        )\n      )\n    )\n  )\n  (layer2): Sequential(\n    (0): ColaSEBottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): ColaSELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (conv): ModuleList(\n          (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        )\n        (bn): ModuleList(\n          (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        )\n        (relu): ReLU(inplace=True)\n        (fc): ModuleList(\n          (0-3): 4 x Sequential(\n            (0): Linear(in_features=128, out_features=128, bias=False)\n            (1): ReLU(inplace=True)\n            (2): Linear(in_features=128, out_features=128, bias=False)\n            (3): Sigmoid()\n          )\n        )\n      )\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaSEBottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): ColaSELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (conv): ModuleList(\n          (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        )\n        (bn): ModuleList(\n          (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        )\n        (relu): ReLU(inplace=True)\n        (fc): ModuleList(\n          (0-3): 4 x Sequential(\n            (0): Linear(in_features=128, out_features=128, bias=False)\n            (1): ReLU(inplace=True)\n            (2): Linear(in_features=128, out_features=128, bias=False)\n            (3): Sigmoid()\n          )\n        )\n      )\n    )\n  )\n  (layer3): Sequential(\n    (0): ColaSEBottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): ColaSELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (conv): ModuleList(\n          (0-2): 3 x Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        )\n        (bn): ModuleList(\n          (0-2): 3 x BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        )\n        (relu): ReLU(inplace=True)\n        (fc): ModuleList(\n          (0-3): 4 x Sequential(\n            (0): Linear(in_features=256, out_features=256, bias=False)\n            (1): ReLU(inplace=True)\n            (2): Linear(in_features=256, out_features=256, bias=False)\n            (3): Sigmoid()\n          )\n        )\n      )\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaSEBottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): ColaSELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (conv): ModuleList(\n          (0-2): 3 x Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        )\n        (bn): ModuleList(\n          (0-2): 3 x BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        )\n        (relu): ReLU(inplace=True)\n        (fc): ModuleList(\n          (0-3): 4 x Sequential(\n            (0): Linear(in_features=256, out_features=256, bias=False)\n            (1): ReLU(inplace=True)\n            (2): Linear(in_features=256, out_features=256, bias=False)\n            (3): Sigmoid()\n          )\n        )\n      )\n    )\n  )\n  (layer4): Sequential(\n    (0): ColaSEBottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): ColaSELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (conv): ModuleList(\n          (0-2): 3 x Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        )\n        (bn): ModuleList(\n          (0-2): 3 x BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        )\n        (relu): ReLU(inplace=True)\n        (fc): ModuleList(\n          (0-3): 4 x Sequential(\n            (0): Linear(in_features=512, out_features=512, bias=False)\n            (1): ReLU(inplace=True)\n            (2): Linear(in_features=512, out_features=512, bias=False)\n            (3): Sigmoid()\n          )\n        )\n      )\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaSEBottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (se): ColaSELayer(\n        (avg_pool): AdaptiveAvgPool2d(output_size=1)\n        (conv): ModuleList(\n          (0-2): 3 x Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n        )\n        (bn): ModuleList(\n          (0-2): 3 x BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        )\n        (relu): ReLU(inplace=True)\n        (fc): ModuleList(\n          (0-3): 4 x Sequential(\n            (0): Linear(in_features=512, out_features=512, bias=False)\n            (1): ReLU(inplace=True)\n            (2): Linear(in_features=512, out_features=512, bias=False)\n            (3): Sigmoid()\n          )\n        )\n      )\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"import torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\n# 这里需要修改优化器\noptimizer = torch.optim.Adam(cola_net.parameters(), lr=0.005)\nprint(len(train_loader))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T11:06:12.675902Z","iopub.execute_input":"2023-05-21T11:06:12.676249Z","iopub.status.idle":"2023-05-21T11:06:12.684660Z","shell.execute_reply.started":"2023-05-21T11:06:12.676219Z","shell.execute_reply":"2023-05-21T11:06:12.683517Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"1250\n","output_type":"stream"}]},{"cell_type":"code","source":"from tqdm import tqdm\ndef train(net, epoch):\n    net.train()\n    # Loop over each batch from the training set\n    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n    for batch_idx, (data, target) in enumerate(train_tqdm):\n        # Copy data to GPU if needed\n        data = data.to(device)\n        target = target.to(device)\n        # Zero gradient buffers\n        optimizer.zero_grad()\n        # Pass data through the network\n        output = net(data)\n        # Calculate loss\n        loss = criterion(output, target)\n        # Backpropagate\n        loss.backward()\n        # Update weights\n        optimizer.step()  # w - alpha * dL / dw\n        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n    return net\n\ndef validate(net, lossv,top1AccuracyList,top5AccuracyList):\n    net.eval()\n    val_loss = 0\n    top1Correct = 0\n    top5Correct = 0\n    for index,(data, target) in enumerate(test_loader):\n        data = data.to(device)\n        labels = target.to(device)\n        outputs = net(data)\n        val_loss += criterion(outputs, labels).data.item()\n        _, top1Predicted = torch.max(outputs.data, 1)\n        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n        top1Correct += (top1Predicted == labels).cpu().sum().item()\n        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n\n    val_loss /= len(test_loader)\n    lossv.append(val_loss)\n\n    top1Acc=100*top1Correct / len(test_loader.dataset)\n    top5Acc=100*top5Correct / len(test_loader.dataset)\n    top1AccuracyList.append(top1Acc)\n    top5AccuracyList.append(top5Acc)\n    \n    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n#     accv.append(accuracy)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T11:06:13.573853Z","iopub.execute_input":"2023-05-21T11:06:13.574209Z","iopub.status.idle":"2023-05-21T11:06:13.588384Z","shell.execute_reply.started":"2023-05-21T11:06:13.574177Z","shell.execute_reply":"2023-05-21T11:06:13.587008Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"import time\n\nres2netLossv = []\nres2netTop1AccuracyList = []   # top1准确率列表\nres2netTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    res2net = train(res2net, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(res2net, res2netLossv,res2netTop1AccuracyList,res2netTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))\n","metadata":{"execution":{"iopub.status.busy":"2023-05-21T01:49:13.011430Z","iopub.execute_input":"2023-05-21T01:49:13.011841Z","iopub.status.idle":"2023-05-21T02:06:15.828626Z","shell.execute_reply.started":"2023-05-21T01:49:13.011805Z","shell.execute_reply":"2023-05-21T02:06:15.827292Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [03:14<00:00,  6.43it/s, loss=2.34]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3122, Top1Accuracy: 4021/10000 (40%) Top5Accuracy: 7104.0/10000 (71%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.63]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2263, Top1Accuracy: 4265/10000 (43%) Top5Accuracy: 7338.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.77] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1583, Top1Accuracy: 4519/10000 (45%) Top5Accuracy: 7467.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.59] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1745, Top1Accuracy: 4616/10000 (46%) Top5Accuracy: 7521.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.23] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2429, Top1Accuracy: 4666/10000 (47%) Top5Accuracy: 7601.0/10000 (76%)\n\nFinished Training\nTraining complete in 16m 11s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\nresnet_bottleneckLossv = []\nresnet_bottleneckTop1AccuracyList = []   # top1准确率列表\nresnet_bottleneckTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_bottleneck = train(resnet_bottleneck, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_bottleneck, resnet_bottleneckLossv,resnet_bottleneckTop1AccuracyList,resnet_bottleneckTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:28:46.022872Z","iopub.execute_input":"2023-05-21T02:28:46.023917Z","iopub.status.idle":"2023-05-21T02:35:44.185983Z","shell.execute_reply.started":"2023-05-21T02:28:46.023866Z","shell.execute_reply":"2023-05-21T02:35:44.184783Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [01:16<00:00, 16.41it/s, loss=3.08]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 4.4866, Top1Accuracy: 3599/10000 (36%) Top5Accuracy: 6654.0/10000 (67%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [01:15<00:00, 16.48it/s, loss=2.47]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.7050, Top1Accuracy: 3805/10000 (38%) Top5Accuracy: 6840.0/10000 (68%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [01:15<00:00, 16.48it/s, loss=2.54]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3556, Top1Accuracy: 4170/10000 (42%) Top5Accuracy: 7197.0/10000 (72%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [01:16<00:00, 16.37it/s, loss=2.09] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2923, Top1Accuracy: 4282/10000 (43%) Top5Accuracy: 7332.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [01:15<00:00, 16.56it/s, loss=1.12] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3284, Top1Accuracy: 4342/10000 (43%) Top5Accuracy: 7364.0/10000 (74%)\n\nFinished Training\nTraining complete in 6m 20s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\nresnet_basicblockLossv = []\nresnet_basicblockTop1AccuracyList = []   # top1准确率列表\nresnet_basicblockTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_basicblock = train(resnet_basicblock, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_basicblock, resnet_basicblockLossv,resnet_basicblockTop1AccuracyList,resnet_basicblockTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:47:46.788808Z","iopub.execute_input":"2023-05-21T02:47:46.789213Z","iopub.status.idle":"2023-05-21T02:51:58.960508Z","shell.execute_reply.started":"2023-05-21T02:47:46.789178Z","shell.execute_reply":"2023-05-21T02:51:58.959190Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [00:43<00:00, 29.07it/s, loss=1.95] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2133, Top1Accuracy: 4380/10000 (44%) Top5Accuracy: 7424.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [00:43<00:00, 28.84it/s, loss=1.24] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.4084, Top1Accuracy: 4323/10000 (43%) Top5Accuracy: 7323.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [00:42<00:00, 29.22it/s, loss=1.14] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.6218, Top1Accuracy: 4388/10000 (44%) Top5Accuracy: 7352.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [00:43<00:00, 28.68it/s, loss=0.478]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.0349, Top1Accuracy: 4320/10000 (43%) Top5Accuracy: 7195.0/10000 (72%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [00:43<00:00, 28.54it/s, loss=0.142] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.0746, Top1Accuracy: 4373/10000 (44%) Top5Accuracy: 7311.0/10000 (73%)\n\nFinished Training\nTraining complete in 3m 37s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\nresnet_seLossv = []\nresnet_seTop1AccuracyList = []   # top1准确率列表\nresnet_seTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_se = train(resnet_se, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_se, resnet_seLossv,resnet_seTop1AccuracyList,resnet_seTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T08:25:33.671119Z","iopub.execute_input":"2023-05-21T08:25:33.671585Z","iopub.status.idle":"2023-05-21T08:33:10.269962Z","shell.execute_reply.started":"2023-05-21T08:25:33.671548Z","shell.execute_reply":"2023-05-21T08:33:10.268799Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [01:23<00:00, 14.91it/s, loss=2.15]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.5215, Top1Accuracy: 3665/10000 (37%) Top5Accuracy: 6653.0/10000 (67%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [01:23<00:00, 14.98it/s, loss=2.55]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.4254, Top1Accuracy: 3793/10000 (38%) Top5Accuracy: 6900.0/10000 (69%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [01:23<00:00, 14.95it/s, loss=1.86]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.5326, Top1Accuracy: 3697/10000 (37%) Top5Accuracy: 6741.0/10000 (67%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [01:22<00:00, 15.07it/s, loss=1.62] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3562, Top1Accuracy: 4100/10000 (41%) Top5Accuracy: 7082.0/10000 (71%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [01:23<00:00, 15.04it/s, loss=1.35] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3838, Top1Accuracy: 4203/10000 (42%) Top5Accuracy: 7194.0/10000 (72%)\n\nFinished Training\nTraining complete in 6m 57s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\ncola_netLossv = []\ncola_netTop1AccuracyList = []   # top1准确率列表\ncola_netTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    cola_net = train(cola_net, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(cola_net, cola_netLossv,cola_netTop1AccuracyList,cola_netTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T11:38:50.382615Z","iopub.execute_input":"2023-05-21T11:38:50.382991Z","iopub.status.idle":"2023-05-21T11:50:35.099054Z","shell.execute_reply.started":"2023-05-21T11:38:50.382956Z","shell.execute_reply":"2023-05-21T11:50:35.097843Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [02:12<00:00,  9.46it/s, loss=1.58] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1989, Top1Accuracy: 4551/10000 (46%) Top5Accuracy: 7564.0/10000 (76%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [02:11<00:00,  9.52it/s, loss=0.975]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2402, Top1Accuracy: 4586/10000 (46%) Top5Accuracy: 7553.0/10000 (76%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [02:11<00:00,  9.49it/s, loss=1.12] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.4154, Top1Accuracy: 4561/10000 (46%) Top5Accuracy: 7492.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [02:11<00:00,  9.52it/s, loss=0.906]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.6014, Top1Accuracy: 4448/10000 (44%) Top5Accuracy: 7399.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [02:11<00:00,  9.49it/s, loss=0.685]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.7565, Top1Accuracy: 4439/10000 (44%) Top5Accuracy: 7383.0/10000 (74%)\n\nFinished Training\nTraining complete in 10m 58s\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}